The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Proceedings of the 11th international conference on World Wide Web
Unifying Keywords and Visual Contents in Image Retrieval
IEEE MultiMedia
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Multimodal concept-dependent active learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Multi-Modal Information Retrieval with a Semantic View Mechanism
AINA '05 Proceedings of the 19th International Conference on Advanced Information Networking and Applications - Volume 1
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance feedback using adaptive clustering for image similarity retrieval
Journal of Systems and Software
Two-scale image retrieval with significant meta-information feedback
Proceedings of the 13th annual ACM international conference on Multimedia
Understanding multimedia document semantics for cross-media retrieval
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
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In the area of image retrieval, search engines are tender to retrieve images that are most relevant to the users’ queries. Nevertheless, in most cases, queries cannot be represented just by several query words. Therefore, it is necessary to provide relevant retrieval results with broad topic-coverage to meet the users’ ambiguous needs. In this paper, a re-ranking method based on topic coverage analysis is proposed to perform the refinement of retrieval results. A graph called Topic Coverage Graph (TCG) is constructed to model the degree of mutual topic coverage among images. Then, Topic Richness Score (TRS), which is calculated based on TCG, is used to measure the importance of each image in improving the topic coverage of image retrieval results. Experimental results on over 20,000 images demonstrate that our proposed approach is effective in improving the topic coverage of retrieval results without loss of relevance.