Multiple object retrieval in image databases using hierarchical segmentation tree
Proceedings of the 19th ACM international conference on Multimedia - MM '11
The purpose of this research is to develop a new visual information analysis, representation, and retrieval framework for automatic discovery of salient objects of user's interest in large-scale image databases. In particular, this dissertation describes a content-based image retrieval framework which supports multiple-object retrieval. The proposed research, unlike most existing works that are designed for single object retrieval or adopt heuristic multiple object matching scheme, aims at
... cheme, aims at contributing to this field through the development of an image retrieval system that enables effective and efficient multiple-object retrieval and automatic discovery of the objects of interest through users' relevance feedback. The key to achieving the above goal is a new systematic and hierarchical image representation, and a related learning and retrieval framework, the combination of which makes it possible for a machine to interpret an image in terms of its containing regions and their relationships. In this dissertation, an efficient and accurate hierarchical image segmentation algorithm based on multi-resolution analysis is developed to alleviate the over-and/or under-segmentation problems through the preservation of associative relationships between image regions in a hierarchical regiontree. This algorithm is designed in a way to simultaneously produce image segmentation results and hierarchical region-tree representations, which are typically obtained through two separate processes in existing approaches, so as to reduce the time complexity. With hierarchical region-tree representations, the relevance of an image to the query image is iv thus measured according to the sub-tree comparison. As a full comparison of all sub-trees is unlikely to be feasible, an efficient strategy for selecting and comparing proper subtrees is designed and developed. Another key contribution of this research is the seamless integration of users' relevance feedback (RF) with the proposed multiple object retrieval system, which allows automatic discovery of the objects of users' interest and is expected to improve the retrieval accuracy through feedback-retrieval loops. While there is a clear indication of needs for such interactive learning capabilities, we believe this is the first systematic attempt to formulate a comprehensive, intelligent, and interactive framework for multiple object retrieval in image databases that takes full advantage of a hierarchical region-tree representation. Keywords: content-based image retrieval, multi-resolution image segmentation, hierarchical segmentation tree, multiple object retrieval, relevance feedback v DEDICATION I dedicate this dissertation to my family for their love, support and sacrifice. vi ACKNOWLEDGEMENTS It would not have been possible to write this dissertation without the help and support of the kind people around me, to only some of whom it is possible to give particular mention here. First and foremost, I want to express my utmost gratitude to Dr. Chengcui Zhang for her guidance, inspiration, support and patience in the past seven years. Her mentorship was paramount in providing a well-rounded experience consistent with my long-term career goals. I would like to gratefully and sincerely thank my dissertation committee members, Dr. , for their precious time, constructive comments, and sharing invaluable experience with me.