In recent years Content Based Information Retrieval (CBIR) has received a lot of research attention, starting with audio, followed by images and video. Video ngerprinting is a CBIR technique that creates a digital descriptor, also known as a ngerprint, for videos based on its content. These ngerprints are then saved to a database and used to detect unknown videos by comparing the unknown video's ngerprint to the ngerprints in the database to get a match. Many techniques have already been proposed with various levels of success, but most of the existing techniques focus mainly on robustness and neglect the speed of implementation. In this dissertation a novel video ngerprinting technique will be developed with the main focus on detecting advertisements in a television broadcast. Therefore the system must be able to process the incoming video stream in real-time and detect all the advertisements that are present. Even though the algorithm has to be fast, it still has to be robust enough to handle a moderate amount of distortions. These days video ngerprinting still holds many challenges as it involves characterizing videos, made up of sequences of images, e ectively. This means the algorithm must somehow imitate the inherent ability of humans to recognize a video almost instantly. The technique uses the content of the video to derive a ngerprint, thus the features used by the ngerprinting algorithm should be robust to distortions that don't a ect content according to humans. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013

In recent years Content Based Information Retrieval (CBIR) has received a lot of research attention, starting with audio, followed by images and video. Video ngerprinting is a CBIR technique that creates a digital descriptor, also known as a ngerprint, for videos based on its content. These ngerprints are then saved to a database and used to detect unknown videos by comparing the unknown video's ngerprint to the ngerprints in the database to get a match. Many techniques have already been proposed with various levels of success, but most of the existing techniques focus mainly on robustness and neglect the speed of implementation. In this dissertation a novel video ngerprinting technique will be developed with the main focus on detecting advertisements in a television broadcast. Therefore the system must be able to process the incoming video stream in real-time and detect all the advertisements that are present. Even though the algorithm has to be fast, it still has to be robust enough to handle a moderate amount of distortions. These days video ngerprinting still holds many challenges as it involves characterizing videos, made up of sequences of images, e ectively. This means the algorithm must somehow imitate the inherent ability of humans to recognize a video almost instantly. The technique uses the content of the video to derive a ngerprint, thus the features used by the ngerprinting algorithm should be robust to distortions that don't a ect content according to humans. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013

A social media stream allows users to share user-generated content as well as aggregate different external sources into one single stream. In Enterprise 2.0 such social media streams empower co-workers to share their information and to work efficiently and effectively together while replacing email communication. As more users share information it becomes impossible to read the complete stream leading to an information overload. Therefore, it is crucial to provide the users a personalized stream that suggests important and unread messages. The main characteristic of an Enterprise 2.0 social media stream is that co-workers work together on projects represented by topics: the stream is topic-centered and not user-centered as in public streams such as Facebook or Twitter. A lot of work has been done dealing with recommendation in a stream or for news recommendation. However, none of the current research approaches deal with the characteristics of an Enterprise 2.0 social media stream to recommend messages. The existing systems described in the research mainly deal with news recommendation for public streams and lack the applicability for Enterprise 2.0 social media streams. In this thesis a recommender concept is developed that allows the recommendation of messages in an Enterprise 2.0 social media stream. The basic idea is to extract features from a new message and use those features to compute a relevance score for a user. Additionally, those features are used to learn a user model and then use the user model for scoring new messages. This idea works without using explicit user feedback and assures a high user acceptance because no intense rating of messages is necessary. With this idea a content-based and collaborative-based approach is developed. To reflect the topic-centered streams a topic-specific user model is introduced which learns a user model independently for each topic. There are constantly new terms that occur in the stream of messages. For improving the quality of the recommendation (by finding more relevant messages) the recommender should be able to handle the new terms. Therefore, an approach is developed which adapts a user model if unknown terms occur by using terms of similar users or topics. Also, a short- and long-term approach is developed which tries to detect short-term interests of users. Only if the interest of a user occurs repeatedly over a certain time span are terms transferred to the long-term user model. The approaches are evaluated against a dataset obtained through an Enterprise 2.0 social media stream application. The evaluation shows the overall applicability of the concept. Specifically the evaluation shows that a topic-specific user model outperforms a global user model and also that adapting the user model according to similar users leads to an increase in the quality of the recommendation. Interestingly, the collaborative-based approach cannot reach the quality of the content-based approach.

Recently, very large collections of images and videos have grown rapidly. In parallel with this growth, content-based retrieval and querying the indexed collections are required to access visual information. Three main components of the visual information are color, texture and shape. In this thesis, an XML based content-based image retrieval system is presented that combines three visual descriptors of MPEG-7 and measures similarity of images by applying a distance function. An XML database is used for storing these three descriptors. The system is also extended to support high dimensional indexing for efficient search and retrieval from its XML database. To do this, an index structure, called M-Tree, is implemented which uses weighted Euclidean distance function for similarity measure. Ordered Weighted Aggregation (OWA) operators are used to define the weights of the distance function and to combine three features&rsquo / distance functions into one. The system supports nearest neighbor queries and three types of fuzzy queries / feature-based, image-based and color-based queries. Also it is shown through experimental results and analysis of retrieval effectiveness of querying that the content-based retrieval system is effective in terms of retrieval and scalability.

近年來，隨著數位科技快速的發展，影像資料量迅速的增加，因此影像檢索成為重要的多媒體技術之一。在傳統的影像內容檢索技術中，使用影像低階特徵值，例如顏色(Color)、紋理(Texture)、形狀(Shape)等來描述影像的內容並進行圖片相似度的比對。然而，傳統的影像內容檢索僅提供單張影像查詢，很少研究多張影像的查詢。因此，本研究提出一個可針對多張影像查詢的方法以提供多張影像查詢的影像內容檢索。本研究將影像內容檢索結合社群網絡演算法，使用MPEG-7中相關特徵描述子和SIFT做為主要特徵向量，擷取影像的低階影像特徵，透過特徵相似度計算建立影像之間的網絡，並利用社群網絡演算法找出與多張查詢影像相似的影像。實驗結果顯示所提出的方法可精確的擷取到相似的影像。 / In recent years, with the faster and faster development of computer technology, the number of digital images is grown rapidly so that the Content-Based Image Retrieval has become one of important multimedia technologies. Much research has been done on Content-Based Image Retrieval. However, little research has been done on query by multiple images. This thesis investigates the mechanism for query by multiple images. First, MPEG-7 image features and SIFT are extracted from images. Then, we calculate the similarity of images to construct the proximity graph which represents the similarity structure between images. Last, processing of query by multiple images is achieved based on the social network algorithms. Experimental results indicate the proposed method provides high accuracy and precision.

La recerca presentada en aquesta dissertació glossa sobre transformacions de tempo de gravacions monofòniques de saxo jazz preservant l'expressivitat musical. Es una contribució al processament d'audio basat en el contingut, un camp de recerca que ha emergit recentment com a resposta a la necessitat creixent de gestionar intel·ligentment la creixent quantitat d'informació digital multimedia disponible actualment. S'ha investigat com una execució musical, tocada a un tempo concret, es pot reproduir automàticament a un altre tempo mantenint l'expressivitat. Aquest problema no es pot reduir a aplicar una transformació uniforme a totes les notes de la melodia, operació que degradaria la qualitat de l'execució. Proposem un sistema de raonament basat en casos per a transformacions de tempo preservant l'expressivitat. La validació del sistema mostra un comportament superior a la transformació uniforme. A m'es, s'han fet contribucions a l'anàlisi de gravacions expressives, CBR, recuperació de melodies i metodologires d'evaluació de models d'expressivitat. / The research presented in this dissertation focuses on expressivity-aware tempo transformations of monophonic audio recordings of saxophone jazz performances. It is a contribution to content-based audio processing, a field of technology that has recently emerged as an answer to the increased need to deal intelligently with the evergrowing amount of digital multimedia information available nowadays. We have investigated the problem of how a musical performance played at a particular tempo can be rendered automatically at another tempo, while preserving naturally sounding expressivity. This problem cannot be reduced to just applying a uniform transformation to all notes of the melody, since it often degrades the musical quality of the performance. We present a case-based reasoning system for expressivity aware tempo transformations. A validation of the system showed superior results compared to uniform transformation. Furthermore, contributions have been made to expressive performance analysis, CBR, melody retrieval, and evaluation methodologies of expressive models.

Avui en dia, la música està esbiaixada cap al consum d'alguns artistes molt populars. Per exemple, el 2007 només l'1% de totes les cançons en format digital va representar el 80% de les vendes. De la mateixa manera, només 1.000 àlbums varen representar el 50% de totes les vendes, i el 80% de tots els àlbums venuts es varen comprar menys de 100 vegades. Es clar que hi ha una necessitat per tal d'ajudar a les persones a filtrar, descobrir, personalitzar i recomanar música, a partir de l'enorme quantitat de contingut musical disponible. Els algorismes de recomanació de música actuals intenten predir amb precisió el que els usuaris demanen escoltar. Tanmateix, molt sovint aquests algoritmes tendeixen a recomanar artistes famosos, o coneguts d'avantmà per l'usuari. Això fa que disminueixi l'eficàcia i utilitat de les recomanacions, ja que aquests algorismes es centren bàsicament en millorar la precisió de les recomanacions. És a dir, tracten de fer prediccions exactes sobre el que un usuari pugui escoltar o comprar, independentment de quant útils siguin les recomanacions generades. En aquesta tesi destaquem la importància que l'usuari valori les recomanacions rebudes. Per aquesta raó modelem la corba de popularitat dels artistes, per tal de poder recomanar música interessant i desconeguda per l'usuari. Les principals contribucions d'aquesta tesi són: (i) un nou enfocament basat en l'anàlisi de xarxes complexes i la popularitat dels productes, aplicada als sistemes de recomanació, (ii) una avaluació centrada en l'usuari, que mesura la importància i la desconeixença de les recomanacions, i (iii) dos prototips que implementen la idees derivades de la tasca teòrica. Els resultats obtinguts tenen una clara implicació per aquells sistemes de recomanació que ajuden a l'usuari a explorar i descobrir continguts que els pugui agradar. / Actualmente, el consumo de música está sesgada hacia algunos artistas muy populares. Por ejemplo, en el año 2007 sólo el 1% de todas las canciones en formato digital representaron el 80% de las ventas. De igual modo, únicamente 1.000 álbumes representaron el 50% de todas las ventas, y el 80% de todos los álbumes vendidos se compraron menos de 100 veces. Existe, pues, una necesidad de ayudar a los usuarios a filtrar, descubrir, personalizar y recomendar música a partir de la enorme cantidad de contenido musical existente. Los algoritmos de recomendación musical existentes intentan predecir con precisión lo que la gente quiere escuchar. Sin embargo, muy a menudo estos algoritmos tienden a recomendar o bien artistas famosos, o bien artistas ya conocidos de antemano por el usuario.Esto disminuye la eficacia y la utilidad de las recomendaciones, ya que estos algoritmos se centran en mejorar la precisión de las recomendaciones. Con lo cuál, tratan de predecir lo que un usuario pudiera escuchar o comprar, independientemente de lo útiles que sean las recomendaciones generadas. En este sentido, la tesis destaca la importancia de que el usuario valore las recomendaciones propuestas. Para ello, modelamos la curva de popularidad de los artistas con el fin de recomendar música interesante y, a la vez, desconocida para el usuario.Las principales contribuciones de esta tesis son: (i) un nuevo enfoque basado en el análisis de redes complejas y la popularidad de los productos, aplicada a los sistemas de recomendación,(ii) una evaluación centrada en el usuario que mide la calidad y la novedad de las recomendaciones, y (iii) dos prototipos que implementan las ideas derivadas de la labor teórica. Los resultados obtenidos tienen importantes implicaciones para los sistemas de recomendación que ayudan al usuario a explorar y descubrir contenidos que le puedan gustar. / Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail.Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations. In this Thesis we stress the importance of the user's perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution. The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user's relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like.

Content-based retrieval of multimedia data has been still an active research area. The efficient retrieval of video data is proven a difficult task for content-based video retrieval systems. In this thesis study, a Content-Based Video Retrieval (CBVR) system that adapts two different index structures, namely Slim-Tree and BitMatrix, for efficiently retrieving videos based on low-level features such as color, texture, shape and motion is presented. The system represents low-level features of video data with MPEG-7 Descriptors extracted from video shots by using MPEG-7 reference software and stored in a native XML database. The low-level descriptors used in the study are Color Layout (CL), Dominant Color (DC), Edge Histogram (EH), Region Shape (RS) and Motion Activity (MA). Ordered Weighted Averaging (OWA) operator in Slim-Tree and BitMatrix aggregates these features to find final similarity between any two objects. The system supports three different types of queries: exact match queries, k-NN queries and range queries. The experiments included in this study are in terms of index construction, index update, query response time and retrieval efficiency using ANMRR performance metric and precision/recall scores. The experimental results show that using BitMatrix along with Ordered Weighted Averaging method is superior in content-based video retrieval systems.

The current technologies for acquisition, storage and transmission of digital data, generate large amounts of data. This quantitative increase is directly proportional to the expansion of multimedia databases, where the bases are part of images. Factors contributing to this expansion is the generation of data access and multimedia, which are frequently used by the population through the media today. Thus, we find a clear need exists for automated systems, capable of dealing with the storage and retrieval of data in a time acceptable to the current standards. To this end, systems are designed for content retrieval of images, where the content is described through its low-level visual features such as shape, texture and color. To have such a system is considered ideal, it must be efficient and effective. The effectiveness will result from the way the information was obtained as a low level of images, considering different conditions of focus, lighting and occlusion. The efficiency is a consequence of the results obtained using the organization of information extracted. The methods of grouping are in one of the useful techniques to reduce the computational complexity of these systems, reducing the computational complexity of the methods implemented, but without losing the representation of information extracted. This work proposes a method for retrieval of images based on content, using appropriate techniques of clustering, a technique for detecting edges and a method to normalize the images in the aspect of enlightenment, to get through it the image descriptors that are robust and can be applied efficiently in a retrieval system for images by content - CBIR (Content Based Image Retrieval). / As tecnologias atuais de aquisição, armazenamento e transmissão de dados digitais geram grandes quantidades de dados. Esse aumento quantitativo é diretamente proporcional à ampliação das bases de dados multimídia, onde se inserem as bases de imagens. Fatores relevantes que contribuem para esta ampliação são o acesso e a geração de dados multimídia, os quais são freqüentemente utilizados pela população através dos meios de comunicação atuais. Desta forma, percebe-se claramente a necessidade existente por sistemas automatizados, capazes de lidar com o armazenamento e a recuperação destes dados em um tempo aceitável para os padrões atuais. Para este fim, são desenvolvidos sistemas de recuperação de imagens por conteúdo, onde este conteúdo é descrito através de suas características visuais de baixo nível, como forma, textura e cor. Para que um sistema deste tipo seja considerado ideal, ele deve ser eficiente e eficaz. A eficácia será resultado da maneira de como foram obtidas as informações de baixo nível das imagens, considerando diferentes condições de foco, oclusão e iluminação. A eficiência é conseqüência dos resultados obtidos utilizando-se a organização das informações extraídas. Os métodos de agrupamento constituem em uma das técnicas úteis para diminuir a complexidade computacional destes sistemas, uma vez que agrupa informações com características semelhantes, sob determinado critério, porém sem perder a representatividade das informações extraídas. Este trabalho propõe um método para recuperação de imagens baseada em conteúdo, que utiliza apropriadamente as técnicas de agrupamento, uma técnica de detecção de cantos e um método para normalizar as imagens no aspecto da iluminação, visando através disso obter descritores da imagem que sejam robustos e possam ser aplicados eficientemente em um sistema de recuperação de imagens por conteúdo - CBIR(Content Based Image Retrieval).

Made available in DSpace on 2016-06-02T20:25:00Z (GMT). No. of bitstreams: 1 1671.pdf: 987072 bytes, checksum: 0fc35e6fdb5db76dcc3de1c0138a5b5b (MD5) Previous issue date: 2007-12-20 / This dissertation presents the results of an investigation about the consciousness development of pre-service EFL teachers, throughout their first year, that learning the language as prospective teachers is different from learning for general purposes (in language courses, for example). We have analyzed classes in an EFL teacher Education program that presents theory in the study of the target language (Content-based instruction - CBI) since pre-service teacher first year at the university. These undergraduate students carry (mis)beliefs, which cause them disappointment and resistance against the syllabus. However, in the end of the first year it is possible to notice the beginning of a consciousness process in some participants. Our study shows that the CBI is appropriate for EFL teacher education. Nevertheless there should be a smoother process of consciousness raising of students that they are in a different position than that of mere language learners, so that they can notice this new role by themselves / Este trabalho trata da investigação sobre o desenvolvimento da conscientização do professor pré-serviço de língua estrangeira (inglês), ao longo do primeiro ano, de que aprender língua como um futuro professor da mesma é diferente de aprender língua para propósitos gerais, em cursos de idiomas, por exemplo. Coletamos dados em um curso de formação que visa a preparar o aluno desde o primeiro ano para estudar teorias sobre os processos de ensino-aprendizagem e por meio dessas teorias (conteúdo de formação profissional Content-based instruction) desenvolver a competência lingüística. Analisamos o envolvimento e a aceitação do aluno em relação a esse modelo. Percebemos que, no início, os alunos trazem crenças arraigadas e passam por um processo de decepção. Contudo, no final do primeiro ano já se percebem indícios de conscientização de alguns sujeitos. O estudo mostra que a formação de professores de língua inglesa com base em conteúdo é apropriado, mas que é preciso haver cuidado na preparação de terreno e no trabalho de conscientização e desconstrução de conceitos com os alunos