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Localized Content-Based Image Retrieval

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5 Author(s)
Rahmani, R. ; Washington Univ., St. Louis, MO ; Goldman, S.A. ; Hui Zhang ; Cholleti, S.R.
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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.

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:30 ,  Issue: 11 )