Abstract
In this paper, we propose a Semi-Supervised Multiple-Instance Learning (SSMIL) algorithm, and apply it to Localized Content-Based Image Retrieval(LCBIR), where the goal is to rank all the images in the database, according to the object that users want to retrieve. SSMIL treats LCBIR as a Semi-Supervised Problem and utilize the unlabeled pictures to help improve the retrieval performance. The comparison result of SSMIL with several state-of-art algorithms is promising.
The work was supported by the National Science Foundation of China (60475001, 60605002).
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Zhang, D., Shi, Z., Song, Y., Zhang, C. (2007). Localized Content-Based Image Retrieval Using Semi-Supervised Multiple Instance Learning. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_16
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DOI: https://doi.org/10.1007/978-3-540-76386-4_16
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