Unifying textual and visual cues for content-based image retrieval on the World Wide Web
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Analysis of a very large web search engine query log
ACM SIGIR Forum
A unified framework for semantics and feature based relevance feedback in image retrieval systems
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visually Searching the Web for Content
IEEE MultiMedia
Unifying Keywords and Visual Contents in Image Retrieval
IEEE MultiMedia
Mining longitudinal web queries: trends and patterns
Journal of the American Society for Information Science and Technology
Cortina: a system for large-scale, content-based web image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
A Flexible and Extensible Framework for Web Image Retrieval System
AICT-ICIW '06 Proceedings of the Advanced Int'l Conference on Telecommunications and Int'l Conference on Internet and Web Applications and Services
Narrowing the semantic gap - improved text-based web document retrieval using visual features
IEEE Transactions on Multimedia
A unified framework for image retrieval using keyword and visual features
IEEE Transactions on Image Processing
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The multi-modal characteristics of Web image make it possible to unify keywords and visual features for image retrieval in Web context. Most of the existing methods about the integration of these two features focus on the interactive relevance feedback technique, which needs the user’s interaction (i.e. a two-step interactive search). In this paper, an approach based on association rule and clustering techniques is proposed to unify keywords and visual features in a different manner, which seamlessly implements the integration within one-step search. The proposed approach considers both Query By Keyword (QBK) mode and Query By Example (QBE) mode and need not the user’s interaction. The experiment results show the proposed approach remarkably improve the retrieval performance compared with the pure search only based on keywords or visual features, and achieve a retrieval performance approximate to the two-step interactive search without requiring the user’s additional interaction.