EFFECTIVE COMBINING OF COLOR AND TEXTURE DESCRIPTORS FOR INDOOR-OUTDOOR IMAGE CLASSIFICATION

Although many indoor-outdoor image classification methods have been proposed in the literature, most of them have omitted comparison with basic methods to justify the need for complex feature extraction and classification procedures. In this paper we propose a relatively simple but highly accurate method for indoor-outdoor image classification, based on combination of carefully engineered MPEG-7 color and texture descriptors. In order to determine the optimal combination of descriptors which is characterized by efficient extraction, compact representation and high accuracy, we conducted comprehensive empirical tests over several color and texture descriptors. The optimal descriptors combination was used for training and testing of a binary SVM classifier. We have shown that the proper descriptors preprocessing before SVM classification has significant impact on the final result. Comprehensive experimental evaluation shows that the proposed method outperforms several more complex indoor-outdoor image classification techniques on a couple of public datasets

Organising a photograph collection based on human appearance

This thesis describes a complete framework for organising digital photographs in an unsupervised manner, based on the appearance of people captured in the photographs. Organising a collection of photographs manually, especially providing the identities of people captured in photographs, is a time consuming task. Unsupervised grouping of images containing similar persons makes annotating names easier (as a group of images can be named at once) and enables quick search based on query by example. The full process of unsupervised clustering is discussed in this thesis. Methods for locating facial components are discussed and a technique based on colour image segmentation is proposed and tested. Additionally a method based on the Principal Component Analysis template is tested, too. These provide eye locations required for acquiring a normalised facial image. This image is then preprocessed by a histogram equalisation and feathering, and the features of MPEG-7 face recognition descriptor are extracted. A distance measure proposed in the MPEG-7 standard is used as a similarity measure. Three approaches to grouping that use only face recognition features for clustering are analysed. These are modified k-means, single-link and a method based on a nearest neighbour classifier. The nearest neighbour-based technique is chosen for further experiments with fusing information from several sources. These sources are context-based such as events (party, trip, holidays), the ownership of photographs, and content-based such as information about the colour and texture of the bodies of humans appearing in photographs. Two techniques are proposed for fusing event and ownership (user) information with the face recognition features: a Transferable Belief Model (TBM) and three level clustering. The three level clustering is carried out at “event” level, “user” level and “collection” level. The latter technique proves to be most efficient. For combining body information with the face recognition features, three probabilistic fusion methods are tested. These are the average sum, the generalised product and the maximum rule. Combinations are tested within events and within user collections. This work concludes with a brief discussion on extraction of key images for a representation of each cluster

Classifiers and machine learning techniques for image processing and computer vision

Orientador: Siome Klein GoldensteinTese (doutorado) - Universidade Estadual de Campinas, Instituto da ComputaçãoResumo: Neste trabalho de doutorado, propomos a utilizaçãoo de classificadores e técnicas de aprendizado de maquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional. Os problemas de nosso interesse são: categorização de imagens em duas ou mais classes, detecçãao de mensagens escondidas, distinção entre imagens digitalmente adulteradas e imagens naturais, autenticação, multi-classificação, entre outros. Inicialmente, apresentamos uma revisão comparativa e crítica do estado da arte em análise forense de imagens e detecção de mensagens escondidas em imagens. Nosso objetivo é mostrar as potencialidades das técnicas existentes e, mais importante, apontar suas limitações. Com esse estudo, mostramos que boa parte dos problemas nessa área apontam para dois pontos em comum: a seleção de características e as técnicas de aprendizado a serem utilizadas. Nesse estudo, também discutimos questões legais associadas a análise forense de imagens como, por exemplo, o uso de fotografias digitais por criminosos. Em seguida, introduzimos uma técnica para análise forense de imagens testada no contexto de detecção de mensagens escondidas e de classificação geral de imagens em categorias como indoors, outdoors, geradas em computador e obras de arte. Ao estudarmos esse problema de multi-classificação, surgem algumas questões: como resolver um problema multi-classe de modo a poder combinar, por exemplo, caracteríisticas de classificação de imagens baseadas em cor, textura, forma e silhueta, sem nos preocuparmos demasiadamente em como normalizar o vetor-comum de caracteristicas gerado? Como utilizar diversos classificadores diferentes, cada um, especializado e melhor configurado para um conjunto de caracteristicas ou classes em confusão? Nesse sentido, apresentamos, uma tecnica para fusão de classificadores e caracteristicas no cenário multi-classe através da combinação de classificadores binários. Nós validamos nossa abordagem numa aplicação real para classificação automática de frutas e legumes. Finalmente, nos deparamos com mais um problema interessante: como tornar a utilização de poderosos classificadores binarios no contexto multi-classe mais eficiente e eficaz? Assim, introduzimos uma tecnica para combinação de classificadores binarios (chamados classificadores base) para a resolução de problemas no contexto geral de multi-classificação.Abstract: In this work, we propose the use of classifiers and machine learning techniques to extract useful information from data sets (e.g., images) to solve important problems in Image Processing and Computer Vision. We are particularly interested in: two and multi-class image categorization, hidden messages detection, discrimination among natural and forged images, authentication, and multiclassification. To start with, we present a comparative survey of the state-of-the-art in digital image forensics as well as hidden messages detection. Our objective is to show the importance of the existing solutions and discuss their limitations. In this study, we show that most of these techniques strive to solve two common problems in Machine Learning: the feature selection and the classification techniques to be used. Furthermore, we discuss the legal and ethical aspects of image forensics analysis, such as, the use of digital images by criminals. We introduce a technique for image forensics analysis in the context of hidden messages detection and image classification in categories such as indoors, outdoors, computer generated, and art works. From this multi-class classification, we found some important questions: how to solve a multi-class problem in order to combine, for instance, several different features such as color, texture, shape, and silhouette without worrying about the pre-processing and normalization of the combined feature vector? How to take advantage of different classifiers, each one custom tailored to a specific set of classes in confusion? To cope with most of these problems, we present a feature and classifier fusion technique based on combinations of binary classifiers. We validate our solution with a real application for automatic produce classification. Finally, we address another interesting problem: how to combine powerful binary classifiers in the multi-class scenario more effectively? How to boost their efficiency? In this context, we present a solution that boosts the efficiency and effectiveness of multi-class from binary techniques.DoutoradoEngenharia de ComputaçãoDoutor em Ciência da Computaçã

Swarm-based Descriptor Combination and its Application for Image Classification

In this paper, we deal with the descriptor combination problem in image classification tasks. This problem refers to the definition of an appropriate combination of image content descriptors that characterize different visual properties, such as color, shape and texture. In this paper, we propose to model the descriptor combination as a swarm-based optimization problem, which finds out the set of parameters that maximizes the classification accuracy of the Optimum-Path Forest (OPF) classifier. In our model, a descriptor is seen as a pair composed of a feature extraction algorithm and a suitable distance function. Our strategy here is to combine distance scores defined by different descriptors, as well as to employ them to weight OPF edges, which connect samples in the feature space. An extensive evaluation of several swarm-based optimization techniques was performed. Experimental results have demonstrated the robustness of the proposed combination approach