Non-parametric Kernel Ranking Approach for Social Image Retrieval

National Research Foundation (NRF) Singapore; Ministry of Education, Singapore under its Academic Research Funding Tier

A Two-View Learning Approach for Image Tag Ranking

Singapore Ministry of Education Academic Research Fund Tier

Variational learning of a Dirichlet process of generalized Dirichlet distributions for simultaneous clustering and feature selection

This paper introduces a novel enhancement for unsupervised feature selection based on generalized Dirichlet (GD) mixture models. Our proposal is based on the extension of the finite mixture model previously developed in [1] to the infinite case, via the consideration of Dirichlet process mixtures, which can be viewed actually as a purely nonparametric model since the number of mixture components can increase as data are introduced. The infinite assumption is used to avoid problems related to model selection (i.e. determination of the number of clusters) and allows simultaneous separation of data in to similar clusters and selection of relevant features. Our resulting model is learned within a principled variational Bayesian framework that we have developed. The experimental results reported for both synthetic data and real-world challenging applications involving image categorization, automatic semantic annotation and retrieval show the ability of our approach to provide accurate models by distinguishing between relevant and irrelevant features without over- or under-fitting the data

Bridging the semantic gap in content-based image retrieval.

To manage large image databases, Content-Based Image Retrieval (CBIR) emerged as a new research subject. CBIR involves the development of automated methods to use visual features in searching and retrieving. Unfortunately, the performance of most CBIR systems is inherently constrained by the low-level visual features because they cannot adequately express the user\u27s high-level concepts. This is known as the semantic gap problem. This dissertation introduces a new approach to CBIR that attempts to bridge the semantic gap. Our approach includes four components. The first one learns a multi-modal thesaurus that associates low-level visual profiles with high-level keywords. This is accomplished through image segmentation, feature extraction, and clustering of image regions. The second component uses the thesaurus to annotate images in an unsupervised way. This is accomplished through fuzzy membership functions to label new regions based on their proximity to the profiles in the thesaurus. The third component consists of an efficient and effective method for fusing the retrieval results from the multi-modal features. Our method is based on learning and adapting fuzzy membership functions to the distribution of the features\u27 distances and assigning a degree of worthiness to each feature. The fourth component provides the user with the option to perform hybrid querying and query expansion. This allows the enrichment of a visual query with textual data extracted from the automatically labeled images in the database. The four components are integrated into a complete CBIR system that can run in three different and complementary modes. The first mode allows the user to query using an example image. The second mode allows the user to specify positive and/or negative sample regions that should or should not be included in the retrieved images. The third mode uses a Graphical Text Interface to allow the user to browse the database interactively using a combination of low-level features and high-level concepts. The proposed system and ail of its components and modes are implemented and validated using a large data collection for accuracy, performance, and improvement over traditional CBIR techniques

ABSTRACT Toward Bridging the Annotation-Retrieval Gap in Image Search by a Generative Modeling Approach

While automatic image annotation remains an actively pursued research topic, enhancement of image search through its use has not been extensively explored. We propose an annotation-driven image retrieval approach and argue that under a number of different scenarios, this is very effective for semantically meaningful image search. In particular, our system is demonstrated to effectively handle cases of partially tagged and completely untagged image databases, multiple keyword queries, and example based queries with or without tags, all in near-realtime. Because our approach utilizes extra knowledge from a training dataset, it outperforms state-of-the-art visual similarity based retrieval techniques. For this purpose, a novel structure-composition model constructed from Beta distributions is developed to capture the spatial relationship among segmented regions of images. This model combined with the Gaussian mixture model produces scalable categorization of generic images. The categorization results are found to surpass previously reported results in speed and accuracy. Our novel annotation framework utilizes the categorization results to select tags based on term frequency, term saliency, and a WordNet-based measure of congruity, to boost salient tags while penalizing potentially unrelated ones. A bag of words distance measure based on WordNet is used to compute semantic similarity. The effectiveness of our approach is shown through extensive experiments

High-Dimensional Non-Gaussian Data Clustering using Variational Learning of Mixture Models

Clustering has been the topic of extensive research in the past. The main concern is to automatically divide a given data set into different clusters such that vectors of the same cluster are as similar as possible and vectors of different clusters are as different as possible. Finite mixture models have been widely used for clustering since they have the advantages of being able to integrate prior knowledge about the data and to address the problem of unsupervised learning in a formal way. A crucial starting point when adopting mixture models is the choice of the components densities. In this context, the well-known Gaussian distribution has been widely used. However, the deployment of the Gaussian mixture implies implicitly clustering based on the minimization of Euclidean distortions which may yield to poor results in several real applications where the per-components densities are not Gaussian. Recent works have shown that other models such as the Dirichlet, generalized Dirichlet and Beta-Liouville mixtures may provide better clustering results in applications containing non-Gaussian data, especially those involving proportional data (or normalized histograms) which are naturally generated by many applications. Two other challenging aspects that should also be addressed when considering mixture models are: how to determine the model's complexity (i.e. the number of mixture components) and how to estimate the model's parameters. Fortunately, both problems can be tackled simultaneously within a principled elegant learning framework namely variational inference. The main idea of variational inference is to approximate the model posterior distribution by minimizing the Kullback-Leibler divergence between the exact (or true) posterior and an approximating distribution. Recently, variational inference has provided good generalization performance and computational tractability in many applications including learning mixture models. In this thesis, we propose several approaches for high-dimensional non-Gaussian data clustering based on various mixture models such as Dirichlet, generalized Dirichlet and Beta-Liouville. These mixture models are learned using variational inference which main advantages are computational efficiency and guaranteed convergence. More specifically, our contributions are four-fold. Firstly, we develop a variational inference algorithm for learning the finite Dirichlet mixture model, where model parameters and the model complexity can be determined automatically and simultaneously as part of the Bayesian inference procedure; Secondly, an unsupervised feature selection scheme is integrated with finite generalized Dirichlet mixture model for clustering high-dimensional non-Gaussian data; Thirdly, we extend the proposed finite generalized mixture model to the infinite case using a nonparametric Bayesian framework known as Dirichlet process, so that the difficulty of choosing the appropriate number of clusters is sidestepped by assuming that there are an infinite number of mixture components; Finally, we propose an online learning framework to learn a Dirichlet process mixture of Beta-Liouville distributions (i.e. an infinite Beta-Liouville mixture model), which is more suitable when dealing with sequential or large scale data in contrast to batch learning algorithm. The effectiveness of our approaches is evaluated using both synthetic and real-life challenging applications such as image databases categorization, anomaly intrusion detection, human action videos categorization, image annotation, facial expression recognition, behavior recognition, and dynamic textures clustering