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SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
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A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
An effective region-based image retrieval framework
Proceedings of the tenth ACM international conference on Multimedia
A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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Image Indexing Using Color Correlograms
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ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
A Clustering Based Approach to Efficient Image Retrieval
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
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Support vector machines for histogram-based image classification
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Least significant bit steganography detection with machine learning techniques
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BALAS: Empirical Bayesian learning in the relevance feedback for image retrieval
Image and Vision Computing
Hidden semantic concept discovery in region based image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Expert Systems with Applications: An International Journal
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This work is about Content Based Image Retrieval (CBIR), focusing on developing a Fast And Semantics-Tailored (FAST) image retrieval methodology. Specifically, the contributions of FAST methodology to the CBIR literature include: (1) development of a new indexing method based on fuzzy logic to incorporate color, texture, and shape information into a region based approach to improving the retrieval effectiveness and robustness (2) development of a new hierarchical indexing structure and the corresponding Hierarchical, Elimination-based A* Retrieval algorithm (HEAR) to significantly improve the retrieval efficiency without sacrificing the retrieval effectiveness; it is shown that HEAR is guaranteed to deliver a logarithm search in the average case (3) employment of user relevance feedbacks to tailor the semantic retrieval to each user's individualized query preference through the novel Indexing Tree Pruning (ITP) and Adaptive Region Weight Updating (ARWU) algorithms. Theoretical analysis and experimental evaluations show that FAST methodology holds a great promise in delivering fast and semantics-tailored image retrieval in CBIR.