Recent work in the computational modeling of visual attention has demonstrated that a purely bottom-up approach to identifying salient regions within an image can be successfully applied to diverse and practical problems from target recognition to the placement of advertisement. This paper proposes an application of a combination of computational models of visual attention to the image retrieval problem. We demonstrate that certain shortcomings of existing content-based image retrieval solutions can be addressed by implementing a biologically-motivated, unsupervised way of grouping together images whose salient regions of interest (ROIs) are perceptually similar regardless of the visual contents of other (less relevant) parts of the image. We propose a model in which only the salient regions of an image are encoded as ROIs whose features are then compared against previously seen ROIs and assigned cluster membership accordingly. Experimental results show that the proposed approach works well for several combinations of feature extraction techniques and clustering algorithms, suggesting a promising avenue for future improvements, such as the addition of a top-down component and the inclusion of a relevance feedback mechanism.

This paper formulates the problem of object categorization in the discriminant analysis framework focusing on transforming visual feature data so as to make it conform to the compactness hypothesis in order to improve categorization accuracy. The sought transformation, in turn, is found as a solution to an optimization problem formulated in terms of inter-observation distances only, using the technique of iterative majorization. The proposed approach is suitable for both binary and multiple-class categorization problems, and can be applied as a dimensionality reduction technique. In the latter case, the number of discriminative features is determined automatically since the process of feature extraction is fully embedded in the optimization procedure. Performance tests validate our method on a number of benchmark data sets from the UCI repository, while the experiments in the application of visual object and contentbased image categorization demonstrate very competitive results, asserting the method's capability of producing semantically relevant matches that share the same or synonymous vocabulary with the query category and allowing multiple pertinent category assignment.

Recent research on computational modeling of visual attention has demonstrated that a bottom-up approach to identifying salient regions within an image can be applied to diverse and practical problems for which conventional machine vision techniques have not succeeded in producing robust solutions. This paper proposes a new method for extracting regions of interest (ROIs) from images using models of visual attention. It is presented in the context of improving content-based image retrieval (CBIR) solutions by implementing a biologically-motivated, unsupervised technique of grouping together images whose salient ROIs are perceptually similar. In this paper we focus on the process of extracting the salient regions of an image. The excellent results obtained with the proposed method have demonstrated that the ROIs of the images can be independently indexed for comparison against other regions on the basis of similarity for use in a CBIR solution.

Let the honor of your student be as dear to you as your own, the honor of your colleague as the reverence for your teacher, and the reverence for your teacher as the fear of Heaven. Rabbi Elazar ben Shammua, Pirkei Avot My mentor and dear friend Dr. Oge Marques deserves special thanks. His genuine dedication to learning had an impact on me from the moment this work began. Our many discussions were both academically challenging and enlightening. His advice and support were essential to the successful completion of this research. The guidance of Dr. Borko Furht, not only during the course of this dissertation, but since the start of my undergraduate studies, has been invaluable. It was his encouragement that first motivated me to pursue this degree, and for that I will always be grateful. Dr. Hari Kalva provided thoughtful insight as well as resources without which many of the results in this dissertation would not have been possible to obtain. I truly appreciate his help and support.

This paper presents a hierarchical ensemble learning method applied in the context of multimedia autoannotation. In contrast to the standard multiple-category classification setting that assumes independent, non-overlapping and exhaustive set of categories, the proposed approach models explicitly the hierarchical relationships among target classes and estimates their relevance to a query as a trade-off between the goodness of fit to a given category description and its inherent uncertainty. The promising results of the empirical evaluation confirm the viability of the proposed approach, validated in comparison to several techniques of ensemble learning, as well as with different type of baseline classifiers.

This paper presents a hierarchical semantic ensemble learning method applied in the context of multimedia autoannotation. In contrast to the standard multiplecategory classification setting that assumes independent, non-overlapping and exhaustive set of categories, the proposed approach models explicitly the hierarchical relationships among target classes and estimates their relevance to a query as a trade-off between the goodness of fit to a given category description and its inherent uncertainty. The promising results of the empirical evaluation confirm the viability of the proposed approach, validated in comparison to several techniques of ensemble learning, as well as with different type of baseline classifiers. 1.