title: Localized content-based image retrieval.
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.
authors.affiliation: One Microsoft Way, Redmond, WA 98052, USA. rouholr@microsoft.com
authors.forename: Rouhollah
authors.initials: R
authors.lastname: Rahmani
authors.forename: Sally A
authors.initials: SA
authors.lastname: Goldman
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.forename: Sharath R
authors.initials: SR
authors.lastname: Cholleti
authors.forename: Jason E
authors.initials: JE
authors.lastname: Fritts
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003628:Database Management Systems; D016208:Databases, Factual; D004282:Documentation / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011873:Radiology Information Systems