Automatic Generation of the Initial Query Set for CBIR on the Mobile Web
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.
KeywordsMobile Content Collaborative Filtering Content Based Image Retrieval Mobile Web Relevance Feedback
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- 1.Korea internet White Paper (2003)Google Scholar
- 4.Flickner, M., Sawhney, H., Niblack, W., et al.: Query by image and video content: The QBIC system. IEEE Computer Magazine 28(9), 23–32 (1995)Google Scholar
- 6.Porkaew, K., Chakrabarti, K., Mehrotra, S.: Query Refinement for Multimedia Similarity Retrieval in MARS. In: Proc. 7th ACM Multimedia Conference, November 1999, pp. 235–238 (1999)Google Scholar
- 7.Sarwar, B., et al.: Analysis of Recommendation Algorithms for E-Commerce. In: Proc. ACM E-Commerce Conference, pp. 158–167 (2000)Google Scholar
- 8.Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating Word of Mouth. In: Proc. Conference on Human factors in Computing Systems, pp. 210–217 (1995)Google Scholar
- 9.Zhou, X.S., Huang, T.S.: Relevance feedback for image retrieval: a comprehensive review. ACM Multimedia Systems Journal 8(6), 536–544 (2003), 2Google Scholar
- 10.Wu, L., et al.: FALCON: Feedback Adaptive Loop for Content-Based Retrieval. In: Proc. 26th VLDB Conference, pp. 297–306 (2000)Google Scholar