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Content Provider | IEEE Xplore Digital Library |
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Author | Yang Hu Mingjing Li Nenghai Yu |
Copyright Year | 2008 |
Description | Author affiliation: MOE-Microsoft Key Lab. of MCC, Univ. of Sci. & Technol. of China, Hefei (Yang Hu) |
Abstract | We study the problem of learning to rank images for image retrieval. For a noisy set of images indexed or tagged by the same keyword, we learn a ranking model from some training examples and then use the learned model to rank new images. Unlike previous work on image retrieval, which usually coarsely divide the images into relevant and irrelevant images and learn a binary classifier, we learn the ranking model from image pairs with preference relations. In addition to the relevance of images, we are further interested in what portion of the image is of interest to the user. Therefore, we consider images represented by sets of regions and propose multiple-instance rank learning based on the max margin framework. Three different schemes are designed to encode the multiple-instance assumption. We evaluate the performance of the multiple-instance ranking algorithms on real-word images collected from Flickr - a popular photo sharing service. The experimental results show that the proposed algorithms are capable of learning effective ranking models for image retrieval. |
Starting Page | 1 |
Ending Page | 8 |
File Size | 567965 |
Page Count | 8 |
File Format | |
ISBN | 9781424422425 |
ISSN | 10636919 |
DOI | 10.1109/CVPR.2008.4587352 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2008-06-23 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Image retrieval Feedback Asia Information retrieval Search engines Image classification Animals Calibration Image converters Support vector machines |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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