Recent trends in hierarchic document clustering: a critical review
Information Processing and Management: an International Journal
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
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
The cluster hypothesis revisited
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Exploiting clustering and phrases for context-based information retrieval
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Using clustering and visualization for refining the results of a WWW image search engine
Proceedings of the 1998 workshop on New paradigms in information visualization and manipulation
Re-ranking model based on document clusters
Information Processing and Management: an International Journal
Computer and Robot Vision
Computer Vision
The effectiveness of query-specific hierarchic clustering in information retrieval
Information Processing and Management: an International Journal
SemQuery: Semantic Clustering and Querying on Heterogeneous Features for Visual Data
IEEE Transactions on Knowledge and Data Engineering
Implementing Agglomerative Hierarchic Clustering Algorithms for Use in Document Retrieval
Implementing Agglomerative Hierarchic Clustering Algorithms for Use in Document Retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Exploring the relationship between feature and perceptual visual spaces
Journal of the American Society for Information Science and Technology
Surfing on artistic documents with visually assisted tagging
Proceedings of the international conference on Multimedia
Exploiting contextual spaces for image re-ranking and rank aggregation
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Exploiting clustering approaches for image re-ranking
Journal of Visual Languages and Computing
Exploiting pairwise recommendation and clustering strategies for image re-ranking
Information Sciences: an International Journal
Re-ranking by multi-modal relevance feedback for content-based social image retrieval
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Image re-ranking and rank aggregation based on similarity of ranked lists
Pattern Recognition
Multimedia search reranking: A literature survey
ACM Computing Surveys (CSUR)
A scalable re-ranking method for content-based image retrieval
Information Sciences: an International Journal
Using contextual spaces for image re-ranking and rank aggregation
Multimedia Tools and Applications
Hi-index | 0.00 |
In this paper, we propose a re-ranking algorithm using post-retrieval clustering for content-based image retrieval (CBIR). In conventional CBIR systems, it is often observed that images visually dissimilar to a query image are ranked high in retrieval results. To remedy this problem, we utilize the similarity relationship of the retrieved results via post-retrieval clustering. In the first step of our method, images are retrieved using visual features such as color histogram. Next, the retrieved images are analyzed using hierarchical agglomerative clustering methods (HACM) and the rank of the results is adjusted according to the distance of a cluster from a query. In addition, we analyze the effects of clustering methods, querycluster similarity functions, and weighting factors in the proposed method. We conducted a number of experiments using several clustering methods and cluster parameters. Experimental results show that the proposed method achieves an improvement of retrieval effectiveness of over 10% on average in the average normalized modified retrieval rank (ANMRR) measure.