Spatial and temporal representations for multi-modal visual retrieval

This dissertation studies the problem of finding relevant content within a visual collection according to a specific query by addressing three key modalities: symmetric visual retrieval, asymmetric visual retrieval and cross-modal retrieval, depending on the kind of data to be processed. In symmetric visual retrieval, the query object and the elements in the collection are from the same kind of visual data, i.e. images or videos. Inspired by the human visual perception system, we propose new techniques to estimate visual similarity in image-to-image retrieval datasets based on non-metric functions, improving image retrieval performance on top of state-of-the-art methods. On the other hand, asymmetric visual retrieval is the problem in which queries and elements in the dataset are from different types of visual data. We propose methods to aggregate the temporal information of video segments so that imagevideo comparisons can be computed using similarity functions. When compared in image-to-video retrieval datasets, our algorithms drastically reduce memory storage while maintaining high accuracy rates. Finally, we introduce new solutions for cross-modal retrieval, which is the task in which either the queries or the elements in the collection are non-visual objects. In particular, we study text-image retrieval in the domain of art by introducing new models for semantic art understanding, obtaining results close to human performance. Overall, this thesis advances the state-of-the-art in visual retrieval by presenting novel solutions for some of the key tasks in the field. The contributions derived from this work have potential direct applications in the era of big data, as visual datasets are growing exponentially every day and new techniques for storing, accessing and managing large-scale visual collections are required

Recuperação multimodal e interativa de informação orientada por diversidade

Orientador: Ricardo da Silva TorresTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os métodos de Recuperação da Informação, especialmente considerando-se dados multimídia, evoluíram para a integração de múltiplas fontes de evidência na análise de relevância de itens em uma tarefa de busca. Neste contexto, para atenuar a distância semântica entre as propriedades de baixo nível extraídas do conteúdo dos objetos digitais e os conceitos semânticos de alto nível (objetos, categorias, etc.) e tornar estes sistemas adaptativos às diferentes necessidades dos usuários, modelos interativos que consideram o usuário mais próximo do processo de recuperação têm sido propostos, permitindo a sua interação com o sistema, principalmente por meio da realimentação de relevância implícita ou explícita. Analogamente, a promoção de diversidade surgiu como uma alternativa para lidar com consultas ambíguas ou incompletas. Adicionalmente, muitos trabalhos têm tratado a ideia de minimização do esforço requerido do usuário em fornecer julgamentos de relevância, à medida que mantém níveis aceitáveis de eficácia. Esta tese aborda, propõe e analisa experimentalmente métodos de recuperação da informação interativos e multimodais orientados por diversidade. Este trabalho aborda de forma abrangente a literatura acerca da recuperação interativa da informação e discute sobre os avanços recentes, os grandes desafios de pesquisa e oportunidades promissoras de trabalho. Nós propusemos e avaliamos dois métodos de aprimoramento do balanço entre relevância e diversidade, os quais integram múltiplas informações de imagens, tais como: propriedades visuais, metadados textuais, informação geográfica e descritores de credibilidade dos usuários. Por sua vez, como integração de técnicas de recuperação interativa e de promoção de diversidade, visando maximizar a cobertura de múltiplas interpretações/aspectos de busca e acelerar a transferência de informação entre o usuário e o sistema, nós propusemos e avaliamos um método multimodal de aprendizado para ranqueamento utilizando realimentação de relevância sobre resultados diversificados. Nossa análise experimental mostra que o uso conjunto de múltiplas fontes de informação teve impacto positivo nos algoritmos de balanceamento entre relevância e diversidade. Estes resultados sugerem que a integração de filtragem e re-ranqueamento multimodais é eficaz para o aumento da relevância dos resultados e também como mecanismo de potencialização dos métodos de diversificação. Além disso, com uma análise experimental minuciosa, nós investigamos várias questões de pesquisa relacionadas à possibilidade de aumento da diversidade dos resultados e a manutenção ou até mesmo melhoria da sua relevância em sessões interativas. Adicionalmente, nós analisamos como o esforço em diversificar afeta os resultados gerais de uma sessão de busca e como diferentes abordagens de diversificação se comportam para diferentes modalidades de dados. Analisando a eficácia geral e também em cada iteração de realimentação de relevância, nós mostramos que introduzir diversidade nos resultados pode prejudicar resultados iniciais, enquanto que aumenta significativamente a eficácia geral em uma sessão de busca, considerando-se não apenas a relevância e diversidade geral, mas também o quão cedo o usuário é exposto ao mesmo montante de itens relevantes e nível de diversidadeAbstract: Information retrieval methods, especially considering multimedia data, have evolved towards the integration of multiple sources of evidence in the analysis of the relevance of items considering a given user search task. In this context, for attenuating the semantic gap between low-level features extracted from the content of the digital objects and high-level semantic concepts (objects, categories, etc.) and making the systems adaptive to different user needs, interactive models have brought the user closer to the retrieval loop allowing user-system interaction mainly through implicit or explicit relevance feedback. Analogously, diversity promotion has emerged as an alternative for tackling ambiguous or underspecified queries. Additionally, several works have addressed the issue of minimizing the required user effort on providing relevance assessments while keeping an acceptable overall effectiveness. This thesis discusses, proposes, and experimentally analyzes multimodal and interactive diversity-oriented information retrieval methods. This work, comprehensively covers the interactive information retrieval literature and also discusses about recent advances, the great research challenges, and promising research opportunities. We have proposed and evaluated two relevance-diversity trade-off enhancement work-flows, which integrate multiple information from images, such as: visual features, textual metadata, geographic information, and user credibility descriptors. In turn, as an integration of interactive retrieval and diversity promotion techniques, for maximizing the coverage of multiple query interpretations/aspects and speeding up the information transfer between the user and the system, we have proposed and evaluated a multimodal learning-to-rank method trained with relevance feedback over diversified results. Our experimental analysis shows that the joint usage of multiple information sources positively impacted the relevance-diversity balancing algorithms. Our results also suggest that the integration of multimodal-relevance-based filtering and reranking was effective on improving result relevance and also boosted diversity promotion methods. Beyond it, with a thorough experimental analysis we have investigated several research questions related to the possibility of improving result diversity and keeping or even improving relevance in interactive search sessions. Moreover, we analyze how much the diversification effort affects overall search session results and how different diversification approaches behave for the different data modalities. By analyzing the overall and per feedback iteration effectiveness, we show that introducing diversity may harm initial results whereas it significantly enhances the overall session effectiveness not only considering the relevance and diversity, but also how early the user is exposed to the same amount of relevant items and diversityDoutoradoCiência da ComputaçãoDoutor em Ciência da ComputaçãoP-4388/2010140977/2012-0CAPESCNP

Information theoretic combination of classifiers with application to face detection

Combining several classifiers has become a very active subdiscipline in the field of pattern recognition. For years, pattern recognition community has focused on seeking optimal learning algorithms able to produce very accurate classifiers. However, empirical experience proved that is is often much easier finding several relatively good classifiers than only finding one single very accurate predictor. The advantages of combining classifiers instead of single classifier schemes are twofold: it helps reducing the computational requirements by using simpler models, and it can improve the classification skills. It is commonly admitted that classifiers need to be complementary in order to improve their performances by aggregation. This complementarity is usually termed as diversity in classifier combination community. Although diversity is a very intuitive concept, explicitly using diversity measures for creating classifier ensembles is not as successful as expected. In this thesis, we propose an information theoretic framework for combining classifiers. In particular, we prove by means of information theoretic tools that diversity between classifiers is not sufficient to guarantee optimal classifier combination. In fact, we show that diversity and accuracies of the individual classifiers are generally contradictory: two very accurate classifiers cannot be diverse, and inversely, two very diverse classifiers will necessarily have poor classification skills. In order to tackle this contradiction, we propose a information theoretic score (ITS) that fixes a trade-off between these two quantities. A first possible application is to consider this new score as a selection criterion for extracting a good ensemble in a predefined pool of classifiers. We also propose an ensemble creation technique based on AdaBoost, by taking into account the information theoretic score for iteratively selecting the classifiers. As an illustration of efficient classifier combination technique, we propose several algorithms for building ensembles of Support Vector Machines (SVM). Support Vector Machines are one of the most popular discriminative approaches of pattern recognition and are often considered as state-of-the-art in binary classification. However these classifiers present one severe drawback when facing a very large number of training examples: they become computationally expensive to train. This problem can be addressed by decomposing the learning into several classification tasks with lower computational requirements. We propose to train several parallel SVM on subsets of the complete training set. We develop several algorithms for designing efficient ensembles of SVM by taking into account our information theoretic score. The second part of this thesis concentrates on human face detection, which appears to be a very challenging binary pattern recognition task. In this work, we focus on two main aspects: feature extraction and how to apply classifier combination techniques to face detection systems. We introduce new geometrical filters called anisotropic Gaussian filters, that are very efficient to model face appearance. Finally we propose a parallel mixture of boosted classifier for reducing the false positive rate and decreasing the training time, while keeping the testing time unchanged. The complete face detection system is evaluated on several datasets, showing that it compares favorably to state-of-the-art techniques

Visual Feature Learning

Categorization is a fundamental problem of many computer vision applications, e.g., image classification, pedestrian detection and face recognition. The robustness of a categorization system heavily relies on the quality of features, by which data are represented. The prior arts of feature extraction can be concluded in different levels, which, in a bottom up order, are low level features (e.g., pixels and gradients) and middle/high-level features (e.g., the BoW model and sparse coding). Low level features can be directly extracted from images or videos, while middle/high-level features are constructed upon low-level features, and are designed to enhance the capability of categorization systems based on different considerations (e.g., guaranteeing the domain-invariance and improving the discriminative power). This thesis focuses on the study of visual feature learning. Challenges that remain in designing visual features lie in intra-class variation, occlusions, illumination and view-point changes and insufficient prior knowledge. To address these challenges, I present several visual feature learning methods, where these methods cover the following sub-topics: (i) I start by introducing a segmentation-based object recognition system. (ii) When training data are insufficient, I seek data from other resources, which include images or videos in a different domain, actions captured from a different viewpoint and information in a different media form. In order to appropriately transfer such resources into the target categorization system, four transfer learning-based feature learning methods are presented in this section, where both cross-view, cross-domain and cross-modality scenarios are addressed accordingly. (iii) Finally, I present a random-forest based feature fusion method for multi-view action recognition

Statistical Learning Approaches to Information Filtering

Enabling computer systems to understand human thinking or behaviors has ever been an exciting challenge to computer scientists. In recent years one such a topic, information filtering, emerges to help users find desired information items (e.g.~movies, books, news) from large amount of available data, and has become crucial in many applications, like product recommendation, image retrieval, spam email filtering, news filtering, and web navigation etc.. An information filtering system must be able to understand users' information needs. Existing approaches either infer a user's profile by exploring his/her connections to other users, i.e.~collaborative filtering (CF), or analyzing the content descriptions of liked or disliked examples annotated by the user, ~i.e.~content-based filtering (CBF). Those methods work well to some extent, but are facing difficulties due to lack of insights into the problem. This thesis intensively studies a wide scope of information filtering technologies. Novel and principled machine learning methods are proposed to model users' information needs. The work demonstrates that the uncertainty of user profiles and the connections between them can be effectively modelled by using probability theory and Bayes rule. As one major contribution of this thesis, the work clarifies the ``structure'' of information filtering and gives rise to principled solutions. In summary, the work of this thesis mainly covers the following three aspects: Collaborative filtering: We develop a probabilistic model for memory-based collaborative filtering (PMCF), which has clear links with classical memory-based CF. Various heuristics to improve memory-based CF have been proposed in the literature. In contrast, extensions based on PMCF can be made in a principled probabilistic way. With PMCF, we describe a CF paradigm that involves interactions with users, instead of passively receiving data from users in conventional CF, and actively chooses the most informative patterns to learn, thereby greatly reduce user efforts and computational costs. Content-based filtering: One major problem for CBF is the deficiency and high dimensionality of content-descriptive features. Information items (e.g.~images or articles) are typically described by high-dimensional features with mixed types of attributes, that seem to be developed independently but intrinsically related. We derive a generalized principle component analysis to merge high-dimensional and heterogenous content features into a low-dimensional continuous latent space. The derived features brings great conveniences to CBF, because most existing algorithms easily cope with low-dimensional and continuous data, and more importantly, the extracted data highlight the intrinsic semantics of original content features. Hybrid filtering: How to combine CF and CBF in an ``smart'' way remains one of the most challenging problems in information filtering. Little principled work exists so far. This thesis reveals that people's information needs can be naturally modelled with a hierarchical Bayesian thinking, where each individual's data are generated based on his/her own profile model, which itself is a sample from a common distribution of the population of user profiles. Users are thus connected to each other via this common distribution. Due to the complexity of such a distribution in real-world applications, usually applied parametric models are too restrictive, and we thus introduce a nonparametric hierarchical Bayesian model using Dirichlet process. We derive effective and efficient algorithms to learn the described model. In particular, the finally achieved hybrid filtering methods are surprisingly simple and intuitively understandable, offering clear insights to previous work on pure CF, pure CBF, and hybrid filtering

A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

People increasingly use videos on the Web as a source for learning. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning for the period from 2016 to 2021. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends of design guidelines to produce learning video

Metric learning applied for automatic large scale image classification

In the current Internet world, the numbers of digital images are growing exponentially. As a result, it is very tough to retrieve relevant objects for a given query point. For the past few years, researchers have been contributing different algorithms in the two most common machine learning categories to either cluster or classify images. There are several techniques of supervised classification images depending on the local or global feature representation of images, and on the metric used to calculate the distance (or similarity) between images. Recently many studies have shown the interest to learn a metric rather than use a simple metric a priori (e.g. Euclidean distance). This approach is described in the literature as metric learning. The main objective of this thesis is to use metric learning algorithm in the context of large-scale image classification. In this project, we use a metric learning algorithm which is driven by the nearest neighbors approach and has a competence to improve the generic k Nearest Neighbor (kNN) machine learning algorithm. Even though we get significant improvement on the performance of classification, the computation is very expensive due to the large dimensionality of our input dataset. Thus, we use the dimension reduction technique to reduce dimension and computation time as well. Nevertheless, due to the size of the database, classifying and searching a given query point using metric learning algorithm alone exhaustively is intractable

A framework for cardio-pulmonary resuscitation (CPR) scene retrieval from medical simulation videos based on object and activity detection.

In this thesis, we propose a framework to detect and retrieve CPR activity scenes from medical simulation videos. Medical simulation is a modern training method for medical students, where an emergency patient condition is simulated on human-like mannequins and the students act upon. These simulation sessions are recorded by the physician, for later debriefing. With the increasing number of simulation videos, automatic detection and retrieval of specific scenes became necessary. The proposed framework for CPR scene retrieval, would eliminate the conventional approach of using shot detection and frame segmentation techniques. Firstly, our work explores the application of Histogram of Oriented Gradients in three dimensions (HOG3D) to retrieve the scenes containing CPR activity. Secondly, we investigate the use of Local Binary Patterns in Three Orthogonal Planes (LBPTOP), which is the three dimensional extension of the popular Local Binary Patterns. This technique is a robust feature that can detect specific activities from scenes containing multiple actors and activities. Thirdly, we propose an improvement to the above mentioned methods by a combination of HOG3D and LBP-TOP. We use decision level fusion techniques to combine the features. We prove experimentally that the proposed techniques and their combination out-perform the existing system for CPR scene retrieval. Finally, we devise a method to detect and retrieve the scenes containing the breathing bag activity, from the medical simulation videos. The proposed framework is tested and validated using eight medical simulation videos and the results are presented