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Issue No. 11 - November (2008 vol. 30)
ISSN: 0162-8828
pp: 1902-1912
Rouhollah Rahmani , Washington University, St. Louis
Sally A. Goldman , Washington University, St. Louis
Hui Zhang , Washington University, St. Louis
Sharath R. Cholleti , Washington University, St. Louis
Jason E. Fritts , St. Louis University, St. Louis
We define localized content-based image retrieval as a CBIR task where the user is only interested in a portion of the image, and the rest of the image is irrelevant. In this paper we present a localized CBIR system, Accio, that uses labeled images in conjunction with a multiple-instance learning algorithm to first identify the desired object and weight the features accordingly, and then to rank images in the database using a similarity measure that is based upon only the relevant portions of the image. A challenge for localized CBIR is how to represent the image to capture the content. We present and compare two novel image representations, which extend traditional segmentation-based and salient point-based techniques respectively, to capture content in a localized CBIR setting.
Information Search and Retrieval, Relevance feedback, Machine learning

S. R. Cholleti, R. Rahmani, S. A. Goldman, J. E. Fritts and H. Zhang, "Localized Content-Based Image Retrieval," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 1902-1912, 2008.
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