TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
An Extended Generative Feature Learning Algorithm for Image Recognition |
| 영문제목(English Title) |
An Extended Generative Feature Learning Algorithm for Image Recognition |
| 저자(Author) |
Bin Wang
Chuanjiang Li
Qian Zhang
Jifeng Huang
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| 원문수록처(Citation) |
VOL 11 NO. 08 PP. 3984 ~ 4005 (2017. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.
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| 키워드(Keyword) |
Feature learning
distribution modeling
generative score space
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| 파일첨부 |
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