TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Learning Discriminative Fisher Kernel for Image Retrieval |
| 영문제목(English Title) |
Learning Discriminative Fisher Kernel for Image Retrieval |
| 저자(Author) |
Bin Wang
Xiong Li
Yuncai Liu
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| 원문수록처(Citation) |
VOL 07 NO. 03 PP. 0522 ~ 0538 (2013. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Content based image retrieval has become an increasingly important research topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The retrieval systems rely on a key component, the predefined or learned similarity measures over images. We note that, the similarity measures can be potential improved if the data distribution information is exploited using a more sophisticated way. In this paper, we propose a similarity measure learning approach for image retrieval. The similarity measure, so called Fisher kernel, is derived from the probabilistic distribution of images and is the function over observed data, hidden variable and model parameters, where the hidden variables encode high level information which are powerful in discrimination and are failed to be exploited in previous methods. We further propose a discriminative learning method for the similarity measure, i.e., encouraging the learned similarity to take a large value for a pair of images with the same label and to take a small value for a pair of images with distinct labels. The learned similarity measure, fully exploiting the data distribution, is well adapted to dataset and would improve the retrieval system. We evaluate the proposed method on Corel-1000, Corel5k, Caltech101 and MIRFlickr 25,000 databases. The results show the competitive performance of the proposed method.
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| 키워드(Keyword) |
Image retrieval
Fisher kernel
supervised similarity learning
GMMs
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| 파일첨부 |
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