A symbolic image database system is a system in which a large amount of image data and their related information are represented by both symbolic images and physical images. Spatial relationships are important issues for similarity-based retrieval in many image database applications. How to perceive spatial relationships among the components in a symbolic image is an important criterion to find a match between the symbolic image of the scene object and the one being store as a modal in the symbolic image database. With the popularity of digital cameras and the related image processing software, a sequence of images are often rotated or flipped. That is, those images are transformed in the rotation orientation or the reflection direction. A robust spatial similarity framework should be able to recognize image variants such as translation, scaling, rotation, and arbitrary variants. Current retrieval by spatial similarity algorithms can be classified into symbolic projection methods, geometric methods, and graph-matching methods. Symbolic projection could preserve the useful spatial information of objects, such as width, height, and location. However, many iconic indexing strategies based on symbolic projection are sensitive to rotation or reflection. Therefore, these strategies may miss the qualified images, when the query is issued in the orientation different from the orientation of the database images. To solve this problem, researchers derived the rule of the change of spatial relationships in image transformation, and proposed a function to map the spatial relationship to its related transformed one. However, this mapping consists of several conditional statements, which is time-consuming. Thus, in this dissertation, first, we classify the mapping into three cases and carefully assign a 16-bit unique bit pattern to each spatial relationship. Based on the assignment, we can easily do the mapping through our proposed bit operation, intra-exchange, which is a CPU operation and needs only the complexity of O(1). Moreover, we propose an efficient iconic index strategy, called Unique Bit Pattern matrix strategy (UBP matrix strategy) to record the spatial information. In this way, when doing similarity retrieval, we do not need to reconstruct the original image from the UBP matrix in order to obtain the indexes of the rotated and flipped image. Conversely, we can directly derive the index of the rotated or flipped image from the index of the original one through bit operations and the matrix manipulation. Thus, our proposed strategy can do similarity retrieval without missing the qualified database images. In our performance study, first, we analyze the time complexity of the similarity retrieval process of our proposed strategy. Then, the efficiency of our proposed strategy according to the simulation results is presented. We show that our strategy outperforms those mapping strategies based on different number of objects in an image. According to the different number of objects in an image, the percentage of improvement is between 13.64% and 53.23%.

Os Sistemas de Armazenamento e Comunicação de Imagens Médicas fornecem, para os físicos, médicos e técnicos de um centro de saúde, informações gráficas sobre vários aspectos envolvidos no diagnóstico de cada paciente. Atualmente, os exames médicos produzem uma grande quantidade de informação em função dos equipamentos médicos computadorizados, utilizados principalmente para se obter dados imagens internas dos pacientes. Este trabalho descreve os conceitos utilizados em Sistema de Banco de Dados Orientado a Objetos para manipular imagens medicas, de tal forma que possam ser recuperadas através de consultas, baseadas na descrição de seu conteúdo gráfico. A abordagem tradicional utiliza ícones e atributos textuais, armazenados juntamente com as imagens, para especificar as consultas. Este trabalho utiliza uma nova técnica de modelagem para definir o \"tipo de dado imagem\", que permite decidir, anteriormente à execução da consulta, os dados que possam ser significativos para cada imagem, no instante que esta é armazenada no Banco de Dados. Desta forma, a busca por uma determinada informação pode ser acelerada. durante a avaliação de uma consulta. / Picture Archiving and Communication System (PACS) applied in medical image storage provides graphical information of many aspects of the health, diseases and treatment of each patient for the physician and technicians of a health care center. Nowadays, medical exams can generate a large amount of data due to the computerized medical instruments used to collect the graphical information about the patients. This work describes the concepts used in an Object Oriented Data Base System to deal with medical images, so that it can be retrieved through queries based on the graphic contents of the stored images. The usual approach uses icons and textual attributes stored with the images to specify the queries. This work uses a novel modeling technique to define the \"image data type\", through which it is possible to decide, beforehand the query itself, the valuable data of each image when it is stored in the database, so the search can be accelerated when queries are issued.

Os Sistemas de Armazenamento e Comunicação de Imagens Médicas fornecem, para os físicos, médicos e técnicos de um centro de saúde, informações gráficas sobre vários aspectos envolvidos no diagnóstico de cada paciente. Atualmente, os exames médicos produzem uma grande quantidade de informação em função dos equipamentos médicos computadorizados, utilizados principalmente para se obter dados imagens internas dos pacientes. Este trabalho descreve os conceitos utilizados em Sistema de Banco de Dados Orientado a Objetos para manipular imagens medicas, de tal forma que possam ser recuperadas através de consultas, baseadas na descrição de seu conteúdo gráfico. A abordagem tradicional utiliza ícones e atributos textuais, armazenados juntamente com as imagens, para especificar as consultas. Este trabalho utiliza uma nova técnica de modelagem para definir o \"tipo de dado imagem\", que permite decidir, anteriormente à execução da consulta, os dados que possam ser significativos para cada imagem, no instante que esta é armazenada no Banco de Dados. Desta forma, a busca por uma determinada informação pode ser acelerada. durante a avaliação de uma consulta. / Picture Archiving and Communication System (PACS) applied in medical image storage provides graphical information of many aspects of the health, diseases and treatment of each patient for the physician and technicians of a health care center. Nowadays, medical exams can generate a large amount of data due to the computerized medical instruments used to collect the graphical information about the patients. This work describes the concepts used in an Object Oriented Data Base System to deal with medical images, so that it can be retrieved through queries based on the graphic contents of the stored images. The usual approach uses icons and textual attributes stored with the images to specify the queries. This work uses a novel modeling technique to define the \"image data type\", through which it is possible to decide, beforehand the query itself, the valuable data of each image when it is stored in the database, so the search can be accelerated when queries are issued.

Digital image archiving urgently requires advanced techniques for more efficient storage and retrieval methods because of the increasing amount of digital. Although JPEG supply systems to compress image data efficiently, the problems of how to organize the image database structure for efficient indexing and retrieval, how to index and retrieve image data from DCT compressed domain and how to interpret image data semantically are major obstacles for further development of digital image database system. In content-based image, image analysis is the primary step to extract useful information from image databases. The difficulty in content-based image retrieval is how to summarize the low-level features into high-level or semantic descriptors to facilitate the retrieval procedure. Such a shift toward a semantic visual data learning or detection of semantic objects generates an urgent need to link the low level features with semantic understanding of the observed visual information. To solve such a 'semantic gap' problem, an efficient way is to develop a number of classifiers to identify the presence of semantic image components that can be connected to semantic descriptors. Among various semantic objects, the human face is a very important example, which is usually also the most significant element in many images and photos. The presence of faces can usually be correlated to specific scenes with semantic inference according to a given ontology. Therefore, face detection can be an efficient tool to annotate images for semantic descriptors. In this thesis, a paradigm to process, analyze and interpret digital images is proposed. In order to speed up access to desired images, after accessing image data, image features are presented for analysis. This analysis gives not only a structure for content-based image retrieval but also the basic units ii for high-level semantic image interpretation. Finally, images are interpreted and classified into some semantic categories by semantic object detection categorization algorithm.

Avec la révolution numérique de cette dernière décennie, la quantité de photos numériques mise à disposition de chacun augmente plus rapidement que la capacité de traitement des ordinateurs. Les outils de recherche actuels ont été conçus pour traiter de faibles volumes de données. Leur complexité ne permet généralement pas d'effectuer des recherches dans des corpus de grande taille avec des temps de calculs acceptables pour les utilisateurs. Dans cette thèse, nous proposons des solutions pour passer à l'échelle les moteurs de recherche d'images par le contenu. Dans un premier temps, nous avons considéré les moteurs de recherche automatique traitant des images indexées sous la forme d'histogrammes globaux. Le passage à l'échelle de ces systèmes est obtenu avec l'introduction d'une nouvelle structure d'index adaptée à ce contexte qui nous permet d'effectuer des recherches de plus proches voisins approximées mais plus efficaces. Dans un second temps, nous nous sommes intéressés à des moteurs plus sophistiqués permettant d'améliorer la qualité de recherche en travaillant avec des index locaux tels que les points d'intérêt. Dans un dernier temps, nous avons proposé une stratégie pour réduire la complexité de calcul des moteurs de recherche interactifs. Ces moteurs permettent d'améliorer les résultats en utilisant des annotations que les utilisateurs fournissent au système lors des sessions de recherche. Notre stratégie permet de sélectionner rapidement les images les plus pertinentes à annoter en optimisant une méthode d'apprentissage actif. / In this last decade, would the digital revolution and its ancillary consequence of a massive increases in digital picture quantities. The database size grow much faster than the processing capacity of computers. The current search engine which conceived for small data volumes do not any more allow to make searches in these new corpus with acceptable response times for users.In this thesis, we propose scalable content-based image retrieval engines.At first, we considered automatic search engines where images are indexed with global histograms. Secondly, we were interested in more sophisticated engines allowing to improve the search quality by working with bag of feature. In a last time, we proposed a strategy to reduce the complexity of interactive search engines. These engines allow to improve the results by using labels which the users supply to the system during the search sessions.

Το αντικείμενο της μελέτης αυτής είναι η οργάνωση (ταξινόμηση, αναγνώριση, ανάκτηση κλπ.) βάσεων που περιλαμβάνουν εικόνες (φωτογραφίες) φύλλων δένδρων. Η οργάνωση βασίζεται στο σχήμα των φύλλων και περιλαμβάνει διάφορα στάδια. Το πρώτο στάδιο είναι η εξαγωγή του περιγράμματος και γίνεται με διαδικασίες επεξεργασίας εικόνας που περιλαμβάνουν τεχνικές ομαδοποίησης και κατάτμησης. Από το περίγραμμα του φύλλου εξάγονται χαρακτηριστικά που δίνουν την δυνατότητα αξιόπιστης περιγραφής κάθε φύλλου. Μελετήθηκαν στη διατριβή αυτή οι παρακάτω γνωστές μέθοδοι: Centroid Contour Distance, Angle code (histogram), Chain Code Fourier Descriptors. Προτάθηκαν επίσης και καινούριες μέθοδοι: Pecstrum (pattern spectrum), Multidimension Sequence Similarity Measure (MSSM). Οι παραπάνω μέθοδοι υλοποιήθηκαν. Παράχθηκε κατάλληλο λογισμικό και εφαρμόσθηκαν σε μία βάση εικόνων φύλλων επιλεγμένη από το διαδίκτυο. Η αξιολόγηση των μεθόδων έγινε μέσα από έλεγχο της συνολικής ακρίβειας κατηγοριοποίησης (με τον confusion matrix). H μέθοδος MSSM έδωσε τα καλύτερα αποτελέσματα. Μία οπτική αξιολόγηση έγινε σε αναπαράσταση 2 διαστάσεων (biplot) μέσα απο διαδικασία Multidimensional Scaling. / The objective of this thesis is the leaf images data base organization (i.e classification, recognition, retrieval etc.). The database organization is based on the leaf shape and is accomplished in a few stages. The contour recognition and recording consist the first stage and is performed with image processing operations namely clustering and segmentation. From the leaf contour several features are extracted appropriate for a reliable description of each leaf type. The following well known techniques were studied in this thesis: Centroid Contour Distance, Angle code (histogram), Chain Code, Fourier Descriptors. Two new metods were also proposed: Pecstrum (pattern spectrum), Multidimension Sequence Similarity Measure. In the experimental study appropriate software was produced to realize all the above methods which was applied to the leaf data base downloaded from internet. The overall evaluation of the methods was done by means of the classification in precision and using the confusion matrix. Best results were produced by the MSSM method.

Digital image archiving urgently requires advanced techniques for more efficient storage and retrieval methods because of the increasing amount of digital. Although JPEG supply systems to compress image data efficiently, the problems of how to organize the image database structure for efficient indexing and retrieval, how to index and retrieve image data from DCT compressed domain and how to interpret image data semantically are major obstacles for further development of digital image database system. In content-based image, image analysis is the primary step to extract useful information from image databases. The difficulty in content-based image retrieval is how to summarize the low-level features into high-level or semantic descriptors to facilitate the retrieval procedure. Such a shift toward a semantic visual data learning or detection of semantic objects generates an urgent need to link the low level features with semantic understanding of the observed visual information. To solve such a -semantic gap¿ problem, an efficient way is to develop a number of classifiers to identify the presence of semantic image components that can be connected to semantic descriptors. Among various semantic objects, the human face is a very important example, which is usually also the most significant element in many images and photos. The presence of faces can usually be correlated to specific scenes with semantic inference according to a given ontology. Therefore, face detection can be an efficient tool to annotate images for semantic descriptors. In this thesis, a paradigm to process, analyze and interpret digital images is proposed. In order to speed up access to desired images, after accessing image data, image features are presented for analysis. This analysis gives not only a structure for content-based image retrieval but also the basic units ii for high-level semantic image interpretation. Finally, images are interpreted and classified into some semantic categories by semantic object detection categorization algorithm.

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Random sampling is one of the most widely used means to build synopses of large datasets because random samples can be used for a wide range of analytical tasks. Unfortunately, the quality of the estimates derived from a sample is negatively affected by the presence of 'outliers' in the data. In this paper, we show how to circumvent this shortcoming by constructing outlier-aware sample synopses. Our approach extends the well-known outlier indexing scheme to multiple aggregation columns.