Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
International Journal of Computer Vision
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Re-ranking model based on document clusters
Information Processing and Management: an International Journal
IEEE Transactions on Circuits and Systems for Video Technology
Challenges of Image and Video Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
View influence analysis and optimization for multiview face recognition
Journal on Image and Video Processing
Majority based ranking approach in web image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Automatically detecting and classifying noises in document images
Proceedings of the 2010 ACM Symposium on Applied Computing
A recommender system for assistive environments
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Pseudo relevance feedback based on iterative probabilistic one-class SVMs in web image retrieval
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
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In this paper, we propose a ranking algorithm using dynamic clustering for content-based image retrieval(CBIR). In conventional CBIR systems, it is often observed that visually dissimilar images to the query image are located at high ranking. To remedy this problem, we utilize similarity relationship of retrieved results via dynamic clustering. In the first step of our method, images are retrieved using visual feature such as color histogram, etc. Next, the retrieved images are analyzed using a HACM(Hierarchical Agglomerative Clustering Method) and the ranking of results is adjusted according to distance from a cluster representative to a query.We show the experimental results based on MPEG-7 color test images. According to our experiments, the proposed method achieves more than 10 % improvements of retrieval effectiveness in ANMRR(Average Normalized Modified Retrieval Rank) performance measure.