Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Query refinement for multimedia similarity retrieval in MARS
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
FALCON: Feedback Adaptive Loop for Content-Based Retrieval
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Relevance feedback using adaptive clustering for image similarity retrieval
Journal of Systems and Software
IEEE Transactions on Multimedia
Automatic image representation and clustering on mobile devices
Journal of Mobile Multimedia
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Despite the rapid growth of wallpaper image downloading service in the mobile contents market, users experience high levels of frustration in searching for desired images, due to the absence of intelligent searching aid. Although Content Based Image Retrieval is the most widely used technique for image retrieval in the PC-based system, its application in the mobile Web environment poses one major problem of not being able to satisfy its initial query requirement because of the limitations in user interfaces of the mobile application software. We propose a new approach, so called a CF-fronted CBIR, where Collaborative Filtering (CF) technique automatically generates a list of candidate images that can be used as an initial query in Content Based Image Retrieval (CBIR) by utilizing relevance information captured during Relevance Feedback. The results of the experiment using a PC-based prototype system verified that the proposed approach not only successfully satisfies the initial query requirement of CBIR in the mobile Web environment but also outperforms the current search process.