Faceted metadata for image search and browsing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
FacetBrowser: a user interface for complex search tasks
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Learning tag relevance by neighbor voting for social image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Boost search relevance for tag-based social image retrieval
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Energy Conservation for Image Retrieval on Mobile Systems
ACM Transactions on Embedded Computing Systems (TECS)
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With the popularity of social image-sharing websites, the amount of images uploaded and shared among the users has increased explosively. To allow keyword search, the system constructs an index from image tags assigned by the users. The tag-based image retrieval approach, although very scalable, has some serious drawbacks due to the problems of tag spamming and subjectivity in tagging. In this paper, we propose an approach for improving the tag-based image retrieval by exploiting some techniques in content-based image retrieval (CBIR). Given an image collection, we construct an index based on 130-scale Munsell-based colors. Users are allowed to perform query by keywords with color and/or tone selection. The color index is also used for improving ranking of search results via the user relevance feedback.