Abstract
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.
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References
Korea internet White Paper (2003)
Brunelli, R., Mich, O.: Image Retrieval by Examples. IEEE Transactions on Multimedia 2(3), 164–171 (2000)
Cho, Y.H., Kim, J.K.: Application of Web Usage Mining and Product Taxonomy to Collaborative Recommendations in E-Commerce. Expert Systems with Applications 26(2), 233–246 (2004)
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)
Kim, D.H., Chung, C.W., Barnard, K.: Relevance feedback using adaptive clustering for image similarity retrieval. Journal of Systems and Software 78(1), 9–23 (2005)
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)
Sarwar, B., et al.: Analysis of Recommendation Algorithms for E-Commerce. In: Proc. ACM E-Commerce Conference, pp. 158–167 (2000)
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)
Zhou, X.S., Huang, T.S.: Relevance feedback for image retrieval: a comprehensive review. ACM Multimedia Systems Journal 8(6), 536–544 (2003), 2
Wu, L., et al.: FALCON: Feedback Adaptive Loop for Content-Based Retrieval. In: Proc. 26th VLDB Conference, pp. 297–306 (2000)
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© 2005 Springer-Verlag Berlin Heidelberg
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Kim, D.H., Kim, C.Y., Cho, Y.H. (2005). Automatic Generation of the Initial Query Set for CBIR on the Mobile Web. In: Ho, YS., Kim, H.J. (eds) Advances in Multimedia Information Processing - PCM 2005. PCM 2005. Lecture Notes in Computer Science, vol 3767. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11581772_84
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DOI: https://doi.org/10.1007/11581772_84
Publisher Name: Springer, Berlin, Heidelberg
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