Localized content-based image retrieval.
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.
Rahmani
Rouhollah
R
One Microsoft Way, Redmond, WA 98052, USA. rouholr@microsoft.com
Goldman
Sally A
SA
Zhang
Hui
H
Cholleti
Sharath R
SR
Fritts
Jason E
JE
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