The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
ACM Computing Surveys (CSUR)
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Properties of Embedding Methods for Similarity Searching in Metric Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
QCluster: relevance feedback using adaptive clustering for content-based image retrieval
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Evaluating Refined Queries in Top-k Retrieval Systems
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
BoostMap: a method for efficient approximate similarity rankings
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning distance functions for image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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Existing CBIR systems, designed around query refinement based on relevance feedback, suffer from local optimum traps. That is, when the user is examining a relevant cluster surrounded by less relevant images, essentially the same set of images will be returned for the user to provide relevance feedback. Since the user would select the same query images again, the relevance feedback process gets trapped in a local optimum. This local-optimum trap problem may severely impair the overall retrieval performance of today's CBIR systems. In this paper, we therefore propose a simulated annealing-based approach to address this important issue. When a stuck-at-a-local-optimum occurs, we employ a neighborhood search technique (i.e., simulated annealing) to escape from the local optimum. We also propose an index structure to speed up such neighborhood search. Our experimental study confirms that our approach can efficiently address the local-optimum trap problem, and therefore can improve the effectiveness of existing CBIR systems.