Instance-Based Learning Algorithms
Machine Learning
Dynamic clustering for time incremental data
Pattern Recognition Letters
Active shape models—their training and application
Computer Vision and Image Understanding
Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Probabilistic feature relevance learining for content-based image retrieval
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Indexing shapes in image databases using the centroid-radii model
Data & Knowledge Engineering
PicHunter: Bayesian Relevance Feedback for Image Retrieval
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
A Weighted Distance Approach to Relevance Feedback
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Effective invariant features for shape-based image retrieval: Research Articles
Journal of the American Society for Information Science and Technology
On-line incremental feature weighting in evolving fuzzy classifiers
Fuzzy Sets and Systems
Fast content-based image retrieval based on equal-average k-nearest-neighbor search schemes
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Image retrieval based on a multipurpose watermarking scheme
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
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Similarity between Shapes is often measured by computing the distance between two feature vectors. Unfortunately, the feature space cannot always capture the notion of similarity in human perception. So, most current image retrieval systems use weights measuring the importance of each feature. However, the similarity does not vary with equal strength or in the same proportion in all directions in the feature space. In this paper, we present feature weights based on both clustered objects in the database and on relevance feedback. We show that using variance information from shape clusters to guide cluster information for an initial database search gives better results than using the standard Euclidean distance. To automatically incorporate a user's need, the proposed shape-based query system uses an incremental feature weight learning method that refines prototypes. In contrast to existing image database systems, the system can learn from user feedback. Indexing and retrieval results are presented that demonstrate the efficacy of our technique using the well-known Columbia database.