Localized Content-Based Image Retrieval

Authors:
Rouhollah Rahmani;Sally A. Goldman;Hui Zhang;Sharath R. Cholleti;Jason E. Fritts
Affiliations:
Washington University, St. Louis;Washington University, St. Louis;Washington University, St. Louis;Washington University, St. Louis;St. Louis University, St. Louis
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2008

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Abstract

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.