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. © Springer-Verlag Berlin Heidelberg 2007.
Cite
CITATION STYLE
Zhang, D., Shi, Z., Song, Y., & Zhang, C. (2007). Localized content-based image retrieval using semi-supervised multiple instance learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4843 LNCS, pp. 180–188). Springer Verlag. https://doi.org/10.1007/978-3-540-76386-4_16
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