Multiple Regions and Their Spatial Relationship-Based Image Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
Constraint Based Region Matching for Image Retrieval
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Improving retrieval performance by region constraints and relevance feedback
Journal of Computer Science and Technology
Object-based image retrieval using the statistical structure of images
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Topological active nets for object-based image retrieval
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
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Currently, image retrieval systems are based on low-level features of color, texture and shape, not on the semantic descriptions that are common to humans, such as objects, people, and place. In order to narrow down the gap between the low level and semantic level, object-based content analysis, which segments the semantically meaningful objects of images, is an essential step. In this study, we propose a learning process in order to perform effective automatic off-line analysis on a multi-level segmented image stack. Meaningful objects are extracted given certain user search patterns and interest profiles. Color and/or shape information of the objects is stored in the hierarchical content representations of the images. This information is utilized by a hierarchical matching scheme to improve the retrieval speed in the subsequent searches.