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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
81

Sistematização da percepção médica na construção de sistemas para recuperação de imagens por conteúdo / Systematization of medical perception in implementing of content-based image retrieval systems

Silva, Marcelo Ponciano da 27 February 2014 (has links)
Nos últimos anos o mundo tem vivenciado uma avalanche de novas tecnologias para auxílio ao diagnóstico médico. Esses esforços buscam um diagnóstico rápido e preciso através de exames e informações sobre a condição física do paciente. Através do uso de imagens médicas, a radiologia busca a visualização de órgãos ou estruturas internas do corpo humano para encontrar respostas às suspeitas de problemas físicos expressos por sinais e sintomas relatados pelo paciente. Nessa área, os Sistemas de Comunicação e Armazenamento de Imagens (PACS) têm ajudado no armazenamento e organização do crescente número de imagens geradas pelos exames realizados nos hospitais. Trabalhos de pesquisa médica têm evidenciado o potencial de uso dessas imagens como auxílio à prática da Medicina Baseada em Casos Similares (MBCS). Por esse motivo, há na literatura um esforço contínuo em desenvolver técnicas computacionais para recuperação de imagens baseada em conteúdos similares (CBIR) em grandes conjuntos de dados. As consultas por similaridade são essenciais para apoiar a prática da MBCS e a descoberta de comportamentos de lesões causadas por diversas doenças. A evolução e intensificação das pesquisas em CBIR têm encontrado vários desafios. Um desses é a divergência entre os resultados obtidos automaticamente e aqueles esperados pelos radiologistas (descontinuidade semântica). Outro desafio é a falta de estudos sobre a viabilidade clínica dessas ferramentas como forma de auxílio ao diagnóstico. Esses obstáculos são dois dos principais responsáveis pela não efetivação dessa tecnologia no ambiente médico-hospitalar. Mediante o exposto acima, este trabalho de pesquisa propõe um mecanismo para contornar essa descontinuidade semântica e ao mesmo tempo aproximar o CBIR do ambiente real de aplicação. A contribuição principal deste trabalho foi o desenvolvimento de uma metodologia baseada em parâmetros perceptuais que aproximam o sistema ao nível de percepção do usuário médico. Em seguida, foi realizado um estudo sobre a viabilidade clínica do sistema CBIR no Hospital das Clínicas de Ribeirão Preto. A metodologia proposta foi aplicada e os resultados comprovaram a aplicabilidade de Sistemas CBIR como ferramenta de auxílio ao diagnóstico em um ambiente clínico real / In recent years the world has experienced an avalanche of new technologies to aid medical diagnosis. These efforts seek a quick and accurate diagnosis through exams and information about the patient\'s physical condition. The radiology studies the visualization of the organs or structures through the use of images. In this area, the Picture Archiving and Communication Systems (PACS) have helped in the storage and organization of the growing number of images generated by exams performed in hospitals. Medical research papers have shown the potential use of these images as an aid to the Similar Case-Based Reasoning (SCBR) practice in Medicine. For this reason, there is an ongoing effort in the literature to develop computational techniques for Content-Based Image Retrieval (CBIR) in large data sets. Similarity queries are essential to support the practice of SCBR. The evolution and intensification of research in CBIR have encountered several challenges. One of these is the discrepancy between the results obtained automatically and those expected by radiologists (semantic gap). Another challenge is the lack of studies on the clinical viability of these tools as a way to assist in diagnosis. These obstacles are the two main responsible for reservation in using this technology in the medical hospital environment. Considering this scenario, this research proposes a mechanism to overcome this semantic gap and bring the real environment to the CBIR application. The main contribution for this research was the development of a methodology based on Perceptual Parameters to approximate the system to the level of user perception. Then we conducted a study on the clinical viability of a CBIR system at the Clinical Hospital of the University of São Paulo at Ribeirão Preto. The proposed methodology was applied and the results showed the applicability of CBIR systems as a computer aided diagnosis tool in a real clinical environment
82

Proposta de um histograma perceptual de cores como característica para recuperação de imagens baseada em conteúdo / Proposal of a perception color histogram as characteristic for content-based image retrieval

Silva, Katia Veloso 14 September 2006 (has links)
Este trabalho foi desenvolvido com o intuito de se estabelecer uma metodologia para a classificação das cores de imagens digitais em cores perceptuais para se gerar um vetor de características que permita recuperar imagens através de seu conteúdo em uma base de dados. Em trabalhos e estudos correlatos analisados, as metodologias de agrupamento das diversas cores possíveis de uma imagem não permitem uma associação entre a cor digitalizada e a cor percebida por seres humanos. Estudos mostram que a maioria das culturas humanas associam às cores apenas onze termos: vermelho, amarelo, violeta, azul, verde, rosa, marrom, preto, branco, laranja e cinza. Este trabalho propõe, portanto, uma metodologia baseada em regras da lógica fuzzy, que permite associar a todas as possíveis cores de imagens digitais uma das onze cores culturais definidas, criando assim um histograma perceptual de cores. Isso permitiu a geração de um vetor de características para a recuperação de imagens baseada em conteúdo em uma base de dados. / This work aims at establishing a digital image classification methodology based on perceptual colors, by generating a feature vector that allows retrieving images from a database by their content. In related works the methodologies of grouping the diverse possible colors of an image do not allow associate digitized colors and those colors perceived by human beings. Studies show that the majority of human being culture associates only eleven terms to all the possible colors: red, yellow, blue, green, pink, brown, black, white, purple, orange and gray. This work purpose a methodology based on fuzzy logic that allows to associate the eleven cultural color terms with all of digitized colors by a perceptual color histogram. The image color quantization generates a feature vector used for content-based image retrieval. The results show that it is possible to use the perceptual color histogram for CBIR and in the semantic gap reduction.
83

Análise da influência de funções de distância para o processamento de consultas por similaridade em recuperação de imagens por conteúdo / Analysis of the influence of distance functions to answer similarity queries in content-based image retrieval.

Bugatti, Pedro Henrique 16 April 2008 (has links)
A recuperação de imagens baseada em conteúdo (Content-based Image Retrieval - CBIR) embasa-se sobre dois aspectos primordiais, um extrator de características o qual deve prover as características intrínsecas mais significativas dos dados e uma função de distância a qual quantifica a similaridade entre tais dados. O grande desafio é justamente como alcançar a melhor integração entre estes dois aspectos chaves com intuito de obter maior precisão nas consultas por similaridade. Apesar de inúmeros esforços serem continuamente despendidos para o desenvolvimento de novas técnicas de extração de características, muito pouca atenção tem sido direcionada à importância de uma adequada associação entre a função de distância e os extratores de características. A presente Dissertação de Mestrado foi concebida com o intuito de preencher esta lacuna. Para tal, foi realizada a análise do comportamento de diferentes funções de distância com relação a tipos distintos de vetores de características. Os três principais tipos de características intrínsecas às imagens foram analisados, com respeito a distribuição de cores, textura e forma. Além disso, foram propostas duas novas técnicas para realização de seleção de características com o desígnio de obter melhorias em relação à precisão das consultas por similaridade. A primeira técnica emprega regras de associação estatísticas e alcançou um ganho de até 38% na precisão, enquanto que a segunda técnica utilizando a entropia de Shannon alcançou um ganho de aproximadamente 71% ao mesmo tempo em que reduz significantemente a dimensionalidade dos vetores de características. O presente trabalho também demonstra que uma adequada utilização das funções de distância melhora efetivamente os resultados das consultas por similaridade. Conseqüentemente, desdobra novos caminhos para realçar a concepção de sistemas CBIR / The retrieval of images by visual content relies on a feature extractor to provide the most meaningful intrinsic characteristics (features) from the data, and a distance function to quantify the similarity between them. A challenge in this field supporting content-based image retrieval (CBIR) to answer similarity queries is how to best integrate these two key aspects. There are plenty of researching on algorithms for feature extraction of images. However, little attention have been paid to the importance of the use of a well-suited distance function associated to a feature extractor. This Master Dissertation was conceived to fill in this gap. Therefore, herein it was investigated the behavior of different distance functions regarding distinct feature vector types. The three main types of image features were evaluated, regarding color distribution, texture and shape. It was also proposed two new techniques to perform feature selection over the feature vectors, in order to improve the precision when answering similarity queries. The first technique employed statistical association rules and achieve up to 38% gain in precision, while the second one employing the Shannon entropy achieved 71%, while siginificantly reducing the size of the feature vector. This work also showed that the proper use of a distance function effectively improves the similarity query results. Therefore, it opens new ways to enhance the acceptance of CBIR systems
84

Indexation bio-inspirée pour la recherche d'images par similarité / Bio-inspired Indexing for Content-Based Image Retrieval

Michaud, Dorian 16 October 2018 (has links)
La recherche d'images basée sur le contenu visuel est un domaine très actif de la vision par ordinateur, car le nombre de bases d'images disponibles ne cesse d'augmenter.L’objectif de ce type d’approche est de retourner les images les plus proches d'une requête donnée en terme de contenu visuel.Notre travail s'inscrit dans un contexte applicatif spécifique qui consiste à indexer des petites bases d'images expertes sur lesquelles nous n'avons aucune connaissance a priori.L’une de nos contributions pour palier ce problème consiste à choisir un ensemble de descripteurs visuels et de les placer en compétition directe. Nous utilisons deux stratégies pour combiner ces caractéristiques : la première, est pyschovisuelle, et la seconde, est statistique.Dans ce contexte, nous proposons une approche adaptative non supervisée, basée sur les sacs de mots et phrases visuels, dont le principe est de sélectionner les caractéristiques pertinentes pour chaque point d'intérêt dans le but de renforcer la représentation de l'image.Les tests effectués montrent l'intérêt d'utiliser ce type de méthodes malgré la domination des méthodes basées réseaux de neurones convolutifs dans la littérature.Nous proposons également une étude, ainsi que les résultats de nos premiers tests concernant le renforcement de la recherche en utilisant des méthodes semi-interactives basées sur l’expertise de l'utilisateur. / Image Retrieval is still a very active field of image processing as the number of available image datasets continuously increases.One of the principal objectives of Content-Based Image Retrieval (CBIR) is to return the most similar images to a given query with respect to their visual content.Our work fits in a very specific application context: indexing small expert image datasets, with no prior knowledge on the images. Because of the image complexity, one of our contributions is the choice of effective descriptors from literature placed in direct competition.Two strategies are used to combine features: a psycho-visual one and a statistical one.In this context, we propose an unsupervised and adaptive framework based on the well-known bags of visual words and phrases models that select relevant visual descriptors for each keypoint to construct a more discriminative image representation.Experiments show the interest of using this this type of methodologies during a time when convolutional neural networks are ubiquitous.We also propose a study about semi interactive retrieval to improve the accuracy of CBIR systems by using the knowledge of the expert users.
85

Contour Based 3D Biological Image Reconstruction and Partial Retrieval

Li, Yong 28 November 2007 (has links)
Image segmentation is one of the most difficult tasks in image processing. Segmentation algorithms are generally based on searching a region where pixels share similar gray level intensity and satisfy a set of defined criteria. However, the segmented region cannot be used directly for partial image retrieval. In this dissertation, a Contour Based Image Structure (CBIS) model is introduced. In this model, images are divided into several objects defined by their bounding contours. The bounding contour structure allows individual object extraction, and partial object matching and retrieval from a standard CBIS image structure. The CBIS model allows the representation of 3D objects by their bounding contours which is suitable for parallel implementation particularly when extracting contour features and matching them for 3D images require heavy computations. This computational burden becomes worse for images with high resolution and large contour density. In this essence we designed two parallel algorithms; Contour Parallelization Algorithm (CPA) and Partial Retrieval Parallelization Algorithm (PRPA). Both algorithms have considerably improved the performance of CBIS for both contour shape matching as well as partial image retrieval. To improve the effectiveness of CBIS in segmenting images with inhomogeneous backgrounds we used the phase congruency invariant features of Fourier transform components to highlight boundaries of objects prior to extracting their contours. The contour matching process has also been improved by constructing a fuzzy contour matching system that allows unbiased matching decisions. Further improvements have been achieved through the use of a contour tailored Fourier descriptor to make translation and rotation invariance. It is proved to be suitable for general contour shape matching where translation, rotation, and scaling invariance are required. For those images which are hard to be classified by object contours such as bacterial images, we define a multi-level cosine transform to extract their texture features for image classification. The low frequency Discrete Cosine Transform coefficients and Zenike moments derived from images are trained by Support Vector Machine (SVM) to generate multiple classifiers.
86

Recherche multi-descripteurs dans les fonds photographiques numérisés / Multi-descriptor retrieval in digitalized photographs collections

Bhowmik, Neelanjan 07 November 2017 (has links)
La recherche d’images par contenu (CBIR) est une discipline de l’informatique qui vise à structurer automatiquement les collections d’images selon des critères visuels. Les fonctionnalités proposées couvrent notamment l’accès efficace aux images dans une grande base de données d’images ou l’identification de leur contenu par des outils de détection et de reconnaissance d’objets. Ils ont un impact sur une large gamme de domaines qui manipulent ce genre de données, telles que le multimedia, la culture, la sécurité, la santé, la recherche scientifique, etc.Indexer une image à partir de son contenu visuel nécessite d’abord de produire un résumé visuel de ce contenu pour un usage donné, qui sera l’index de cette image dans la collection. En matière de descripteurs d’images, la littérature est désormais trés riche: plusieurs familles de descripteurs existent, et dans chaque famille de nombreuses approches cohabitent. Bon nombre de descripteurs ne décrivant pas la même information et n’ayant pas les mêmes propriétés d’invariance, il peut être pertinent de les combiner de manière à mieux décrire le contenu de l’image. Cette combinaison peut être mise en oeuvre de différentes manières, selon les descripteurs considérés et le but recherché. Dans cette thése, nous nous concentrons sur la famille des descripteurs locaux, avec pour application la recherche d’images ou d’objets par l’exemple dans une collection d’images. Leurs bonnes propriétés les rendent très populaires pour la recherche, la reconnaissance et la catégorisation d'objets et de scènes. Deux directions de recherche sont étudiées:Combinaison de caractéristiques pour la recherche d’images par l’exemple: Le coeur de la thèse repose sur la proposition d’un modèle pour combiner des descripteurs de bas niveau et génériques afin d’obtenir un descripteur plus riche et adapté à un cas d’utilisation donné tout en conservant la généricité afin d’indexer différents types de contenus visuels. L’application considérée étant la recherche par l’exemple, une autre difficulté majeure est la complexité de la proposition, qui doit correspondre à des temps de récupération réduits, même avec de grands ensembles de données. Pour atteindre ces objectifs, nous proposons une approche basée sur la fusion d'index inversés, ce qui permet de mieux représenter le contenu tout en étant associé à une méthode d’accès efficace.Complémentarité des descripteurs: Nous nous concentrons sur l’évaluation de la complémentarité des descripteurs locaux existant en proposant des critères statistiques d’analyse de leur répartition spatiale dans l'image. Ce travail permet de mettre en évidence une synergie entre certaines de ces techniques lorsqu’elles sont jugées suffisamment complémentaires. Les critères spatiaux sont exploités dans un modèle de prédiction à base de régression linéaire, qui a l'avantage de permettre la sélection de combinaisons de descripteurs optimale pour la base considérée mais surtout pour chaque image de cette base. L'approche est évaluée avec le moteur de recherche multi-index, où il montre sa pertinence et met aussi en lumière le fait que la combinaison optimale de descripteurs peut varier d'une image à l'autre.En outre, nous exploitons les deux propositions précédentes pour traiter le problème de la recherche d'images inter-domaines, correspondant notamment à des vues multi-source et multi-date. Deux applications sont explorées dans cette thèse. La recherche d’images inter-domaines est appliquée aux collections photographiques culturelles numérisées d’un musée, où elle démontre son efficacité pour l’exploration et la valorisation de ces contenus à différents niveaux, depuis leur archivage jusqu’à leur exposition ou ex situ. Ensuite, nous explorons l’application de la localisation basée image entre domaines, où la pose d’une image est estimée à partir d’images géoréférencées, en retrouvant des images géolocalisées visuellement similaires à la requête / Content-Based Image Retrieval (CBIR) is a discipline of Computer Science which aims at automatically structuring image collections according to some visual criteria. The offered functionalities include the efficient access to images in a large database of images, or the identification of their content through object detection and recognition tools. They impact a large range of fields which manipulate this kind of data, such as multimedia, culture, security, health, scientific research, etc.To index an image from its visual content first requires producing a visual summary of this content for a given use, which will be the index of this image in the database. From now on, the literature on image descriptors is very rich; several families of descriptors exist and in each family, a lot of approaches live together. Many descriptors do not describe the same information and do not have the same properties. Therefore it is relevant to combine some of them to better describe the image content. The combination can be implemented differently according to the involved descriptors and to the application. In this thesis, we focus on the family of local descriptors, with application to image and object retrieval by example in a collection of images. Their nice properties make them very popular for retrieval, recognition and categorization of objects and scenes. Two directions of research are investigated:Feature combination applied to query-by-example image retrieval: the core of the thesis rests on the proposal of a model for combining low-level and generic descriptors in order to obtain a descriptor richer and adapted to a given use case while maintaining genericity in order to be able to index different types of visual contents. The considered application being query-by-example, another major difficulty is the complexity of the proposal, which has to meet with reduced retrieval times, even with large datasets. To meet these goals, we propose an approach based on the fusion of inverted indices, which allows to represent the content better while being associated with an efficient access method.Complementarity of the descriptors: We focus on the evaluation of the complementarity of existing local descriptors by proposing statistical criteria of analysis of their spatial distribution. This work allows highlighting a synergy between some of these techniques when judged sufficiently complementary. The spatial criteria are employed within a regression-based prediction model which has the advantage of selecting the suitable feature combinations globally for a dataset but most importantly for each image. The approach is evaluated within the fusion of inverted indices search engine, where it shows its relevance and also highlights that the optimal combination of features may vary from an image to another.Additionally, we exploit the previous two proposals to address the problem of cross-domain image retrieval, where the images are matched across different domains, including multi-source and multi-date contents. Two applications of cross-domain matching are explored. First, cross-domain image retrieval is applied to the digitized cultural photographic collections of a museum, where it demonstrates its effectiveness for the exploration and promotion of these contents at different levels from their archiving up to their exhibition in or ex-situ. Second, we explore the application of cross-domain image localization, where the pose of a landmark is estimated by retrieving visually similar geo-referenced images to the query images
87

Indexation de bases d'images : évaluation de l'impact émotionnel / Image databases indexing : emotional impact assessing

Gbehounou, Syntyche 21 November 2014 (has links)
L'objectif de ce travail est de proposer une solution de reconnaissance de l'impact émotionnel des images en se basant sur les techniques utilisées en recherche d'images par le contenu. Nous partons des résultats intéressants de cette architecture pour la tester sur une tâche plus complexe. La tâche consiste à classifier les images en fonction de leurs émotions que nous avons définies "Négative", "Neutre" et "Positive". Les émotions sont liées aussi bien au contenu des images, qu'à notre vécu. On ne pourrait donc pas proposer un système de reconnaissance des émotions performant universel. Nous ne sommes pas sensible aux mêmes choses toute notre vie : certaines différences apparaissent avec l'âge et aussi en fonction du genre. Nous essaierons de nous affranchir de ces inconstances en ayant une évaluation des bases d'images la plus hétérogène possible. Notre première contribution va dans ce sens : nous proposons une base de 350 images très largement évaluée. Durant nos travaux, nous avons étudié l'apport de la saillance visuelle aussi bien pendant les expérimentations subjectives que pendant la classification des images. Les descripteurs, que nous avons choisis, ont été évalués dans leur majorité sur une base consacrée à la recherche d'images par le contenu afin de ne sélectionner que les plus pertinents. Notre approche qui tire les avantages d'une architecture bien codifiée, conduit à des résultats très intéressants aussi bien sur la base que nous avons construite que sur la base IAPS, qui sert de référence dans l'analyse de l'impact émotionnel des images. / The goal of this work is to propose an efficient approach for emotional impact recognition based on CBIR techniques (descriptors, image representation). The main idea relies in classifying images according to their emotion which can be "Negative", "Neutral" or "Positive". Emotion is related to the image content and also to the personnal feelings. To achieve our goal we firstly need a correct assessed image database. Our first contribution is about this aspect. We proposed a set of 350 diversifed images rated by people around the world. Added to our choice to use CBIR methods, we studied the impact of visual saliency for the subjective evaluations and interest region segmentation for classification. The results are really interesting and prove that the CBIR methods are usefull for emotion recognition. The chosen desciptors are complementary and their performance are consistent on the database we have built and on IAPS, reference database for the analysis of the image emotional impact.
88

Inclusão de diversidade em consultas aos vizinhos mais próximos usando descritores distintos para similaridade e diversidade

Cardoso, Ana Claudia 18 April 2017 (has links)
Submitted by Aelson Maciera (aelsoncm@terra.com.br) on 2017-09-13T18:11:26Z No. of bitstreams: 1 DissACC.pdf: 1668214 bytes, checksum: 82bf6ff6918613ce74cc691a951a22b0 (MD5) / Approved for entry into archive by Ronildo Prado (bco.producao.intelectual@gmail.com) on 2018-01-25T17:53:52Z (GMT) No. of bitstreams: 1 DissACC.pdf: 1668214 bytes, checksum: 82bf6ff6918613ce74cc691a951a22b0 (MD5) / Approved for entry into archive by Ronildo Prado (bco.producao.intelectual@gmail.com) on 2018-01-25T17:54:04Z (GMT) No. of bitstreams: 1 DissACC.pdf: 1668214 bytes, checksum: 82bf6ff6918613ce74cc691a951a22b0 (MD5) / Made available in DSpace on 2018-01-25T18:00:35Z (GMT). No. of bitstreams: 1 DissACC.pdf: 1668214 bytes, checksum: 82bf6ff6918613ce74cc691a951a22b0 (MD5) Previous issue date: 2017-04-18 / Não recebi financiamento / One of the ways to recover images in a database is through similarity queries. Using characteristics extracted from these images, such as color, shape or texture, this work seeks to identify similarities to a central query element. However, the results may be very similar to each other, which is not always the expected result. In addition to the redundancy in the results, the problem of the ’semantic gap’, which is a divergence in the evaluation of similarity between images performed by the computer considering its numerical representation (low level characteristics) and the human perception about the image (high level characteristics). In order to improve the quality of the results, we sought to minimize the issue of redundancy and the ’semantic gap’ through the use of more than one descriptor in queries for similarity. We sought to explore the inclusion of diversity using one descriptor to treat similarity and another descriptor to treat diversity, more generally a metric space for similarity and another for diversity. For the implementation of the query by similarity was used the consultation to several neighbors closer. Considering that the descriptors may be distinct and one of them may have greater numerical representativeness, it was necessary to do the normalization, considering the methods of normalization by the greater distance and normalization by the greater approximate distance with balancing by the intrinsic dimension. An exhaustive search algorithm was used to perform the tests. The experiments were carried out in a classified database. To evaluate the semantic quality of the results, a measure was proposed that evaluates the inclusion of diversity considering the diversity present in the query only considering the similarity and the maximum diversity that can be included. A comparison was made between the result obtained and the considered ideal, which refers to the value of l defined by the user himself. By comparing the results obtained with the results obtained in the queries for a single descriptor, the evaluation of the included diversity followed the trend of l, which allows to say that normalization and balancing is necessary. In addition, it is intended in the future to study new ways of normalizing. / Uma das formas para se recuperar imagens em banco de dados, é através de consultas por similaridade. Utilizando características extraídas dessas imagens, como cor, forma ou textura, busca-se identificar semelhanças a um elemento central de consulta. No entanto, os resultados nas consultas podem ser muito semelhantes entre si, o que nem sempre é o resultado esperado. Além da redundância nos resultados, deve-se destacar o problema do ‘gap semântico’, que é a divergência na avaliação da similaridade entre imagens realizada pelo computador considerando a sua representação numérica (características de baixo nível) e a percepção humana sobre a imagem (características de alto nível). Com o objetivo de melhorar a qualidade dos resultados nas consultas buscou-se minimizar a questão da redundância e do ‘gap semântico’ através da utilização de mais de um descritor nas consultas por similaridade. Buscou-se explorar a inclusão de diversidade utilizando-se um descritor para tratar a similaridade e outro descritor para tratar a diversidade, mais genericamente, um espaço métrico para similaridade e outro para a diversidade. Para a implementação da consulta por similaridade utilizou-se a consulta aos vizinhos diversos mais próximos. Considerando-se que os descritores utilizados podem ser distintos e que um deles possa ter maior representatividade numérica do que o outro, foi necessário fazer a normalização, sendo considerados os métodos da normalização pela maior distância e normalização pela maior distancia aproximada com balanceamento pela dimensão intrínseca. Para a realização dos testes utilizou-se um algoritmo de busca exaustiva. Os experimentos foram realizados em uma base de dados classificada. Para avaliar a qualidade semântica dos resultados foi proposta uma medida que avalia a inclusão de diversidade considerando a diversidade presente na consulta apenas considerando a similaridade e a diversidade máxima que pode ser incluída. Foi feita uma comparação entre o resultado obtido e o considerado ideal, que refere-se ao valor de l definido pelo próprio usuário. Comparando-se os resultados alcançados com os resultados obtidos nas consultas para um único descritor, a avaliação da diversidade incluída acompanhou a tendência de l, o que permite dizer que a normalização e balanceamento é necessário. Além disso, pretende-se futuramente estudar novas formas de normalizar.
89

Mineração visual de imagens aliada a consultas pelos k-vizinhos diversos mais próximos: flexibilizando e maximizando o entendimento de consultas por conteúdo de imagens / Mineração visual de imagens aliada a consultas pelos k-vizinhos diversos mais próximos: flexibilizando e maximizando o entendimento de consultas por conteúdo de imagens

Dias, Rafael Loosli 23 August 2013 (has links)
Made available in DSpace on 2016-06-02T19:06:11Z (GMT). No. of bitstreams: 1 5726.pdf: 4603491 bytes, checksum: 0fe3fa824a018f481106303c4816bf07 (MD5) Previous issue date: 2013-08-23 / Financiadora de Estudos e Projetos / Content-Based Image Retrieval systems use visual information like color, shape and texture to represent images in feature vectors. The numerical representation found for the images is used in query execution through a metric to evaluate the distance between vectors. In general, there is an inconsistency in the evaluation of similarity between images according to human perception and the results computed by CBIR systems, which is called Semantic Gap. One way to overcome this problem is by the addition of a diversity factor in query execution, allowing the user to specify a degree of dissimilarity between the resulting images and changing the query result. Adding diversity in consultation, however, requires high computational cost and the reduction of possible subsets to be analyzed is a difficult task to be understood by the user. This masters degree thesis aims to make use of Visual Data Mining techniques applied to queries in CBIR systems, improving the interpretability of the measure of similarity and diversity, as well as the relevance of the result according to the judgment and prior knowledge of the user. The user takes an active role in the retrieval of images by their content, guiding its result and, consequently, reducing the Semantic Gap. Additionally, a better understanding of the diversity and similarity factors involved in the query is supported by visualization and interaction techniques. / Sistemas de recuperação de imagens por conteúdo (do Inglês, Content-Based Image Retrieval - CBIR) utilizam informações visuais de cor, forma e textura para representar as imagens em vetores de características. A representação numérica encontrada para as imagens é utilizada na execução da consulta através de uma métrica que avalie a distância entre os vetores. Em geral, existe uma inconsistência entre a percepção do ser humano na avaliação de similaridade entre imagens se comparada com a computada por sistemas CBIR, sendo esta descontinuidade denominada Gap Semântico. Adicionar um fator de diversidade na consulta tem-se mostrado como uma maneira de superar este problema, permitindo que o usuário especifique o grau de dissimilaridade entre as imagens resultantes e altere o resultado da consulta. Adicionar diversidade em consulta, no entanto, requer alto custo computacional e a redução das possibilidades de conjuntos para resposta é de difícil entendimento para o usuário. Este trabalho de mestrado propôs a utilização de técnicas de Mineração Visual de Dados (MVD) aplicadas sobre consultas em sistemas CBIR, melhorando a interpretabilidade da medida de similaridade e diversidade, assim como a relevância do resultado obtido. O usuário passa a exercer um papel ativo na consulta por conteúdo de imagens, permitindo que o mesmo dirija o processo, aproximando o resultado ao esperado pela cognição humana e reduzindo o gap semântico.
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Reconhecimento de formas utilizando modelos de compressão de dados e espaços de escalas de curvatura

Lordão, Fernando Augusto Ferreira 27 August 2009 (has links)
Made available in DSpace on 2015-05-14T12:36:54Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2499209 bytes, checksum: 80d399f8f00f3e82d2a3b34e52fd6b05 (MD5) Previous issue date: 2009-08-27 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / As the processing power of computers increases, the quantity and complexity of stored data have growing in the same way, requiring more sophisticated mechanisms to accomplish retrieval with efficacy and efficiency over these information. In image processing, it has become common the retrieval based on its own content, namely Content-Based Image Retrieval (CBIR), which eliminates the need to place additional annotations as textual descriptions and keywords registered by an observer. The purpose of this work is the development of an image retrieval mechanism based on shape recognition. The mechanism consists in (1) compute the Full Curvature Scale Space (FullCSS) image descriptors; and (2) apply over them a lossless compression method objecting to (3) classify these descriptors and retrieve the corresponding images. The FullCSS descriptors register the curvature variations on the image contour indicating the degree and the signal of these variations, which allow identifying where the curvature is concave or convex. The adopted compression method uses the Prediction by Partial Matching (PPM) compression model, which has been successfully used in other works to classify texture images. The results obtained show that this novel approach is able to reach competitive levels of efficacy and efficiency when compared to other works recently developed in this same area. / Com o aumento do poder de processamento dos computadores, cresceu também a quantidade e complexidade dos dados armazenados, exigindo mecanismos cada vez mais sofisticados para se conseguir uma recuperação eficaz e eficiente destas informações. No caso do processamento de imagens, tem se tornado comum a recuperação baseada em seu próprio conteúdo, ou seja, Recuperação de Imagem Baseada em Conteúdo (Content-Based Image Retrieval CBIR), eliminando a necessidade de anotações adicionais como descrições textuais e palavras-chave registradas por um observador. A proposta deste trabalho é o desenvolvimento de um mecanismo de recuperação de imagens através do reconhecimento de sua forma. O mecanismo consiste em (1) calcular os descritores Full Curvature Scale Space (FullCSS) das imagens; e (2) aplicar sobre eles um método de compressão sem perdas com a finalidade de (3) classificar esses descritores e recuperar as imagens correspondentes. Os descritores FullCSS registram as variações na curvatura do contorno da imagem indicando o grau e o sinal dessas variações, permitindo identificar onde a curvatura é côncava ou convexa. O método de compressão adotado utiliza o modelo de compressão Prediction by Partial Matching (PPM), utilizado com sucesso em outros trabalhos para classificar imagens de texturas. Os resultados obtidos indicam que esta abordagem inovadora é capaz de atingir níveis competitivos de eficácia e eficiência quando comparada a outros trabalhos atualmente desenvolvidos nesta mesma área.

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