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Localized Content-Based Image Retrieval Through Evidence Region Identification

12 years 8 months ago
Localized Content-Based Image Retrieval Through Evidence Region Identification
Over the past decade, multiple-instance learning (MIL) has been successfully utilized to model the localized content-based image retrieval (CBIR) problem, in which a bag corresponds to an image and an instance corresponds to a region in the image. However, existing feature representation schemes are not effective enough to describe the bags in MIL, which hinders the adaptation of sophisticated single-instance learning (SIL) methods for MIL problems. In this paper, we first propose an evidence region (or evidence instance) identification method to identify the evidence regions supporting the labels of the images (i.e., bags). Then, based on the identified evidence regions, a very effective feature representation scheme, which is also very computationally efficient and robust to labeling noise, is proposed to describe the bags. As a result, the MIL problem is converted into a standard SIL problem and a support vector machine (SVM) can be easily adapted for localized CBIR...
Wu-Jun Li (Hong Kong University of Science and Tec
Added 09 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Wu-Jun Li (Hong Kong University of Science and Technology), Dit-Yan Yeung (Hong Kong University of Science and Technology)
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