SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
A semantic modeling approach for image retrieval by content
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Knowledge-Based Image Retrieval with Spatial and Temporal Constructs
IEEE Transactions on Knowledge and Data Engineering
An Object Relational Approach to Biomedical Database
BIBE '00 Proceedings of the 1st IEEE International Symposium on Bioinformatics and Biomedical Engineering
Visual media retrieval framework using web service
HSI'05 Proceedings of the 3rd international conference on Human Society@Internet: web and Communication Technologies and Internet-Related Social Issues
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Most of the content-based image retrieval systems focuses on similarity-based retrieval of natural picture images by utilizing color, shape, and texture features. For the neuroscience image databases, we found that retrieving similar images based on global average features is meaningless to pathological researchers. To realize the practical content-based retrieval on images in neuroscience databases, it is essential to represent internal contents or semantics of images in detail. In this paper, we present how to represent image contents and their related concepts to support more useful retrieval on such images. We also describe the operational semantics to support these advanced retrievals by using object-oriented message path expressions. Our schemes are flexible and extensible, enabling users to incrementally add more semantics on image contents for more enhanced content searching.