Large-margin multi-view Gaussian process for image classification
- Publication Type:
- Conference Proceeding
- Citation:
- ACM International Conference Proceeding Series, 2013, pp. 7 - 12
- Issue Date:
- 2013-09-16
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
 | 2013003402OK.pdf | 2.73 MB |
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In image classification, the goal is to decide whether an image belongs to a certain category or not. Multiple features are usually employed to comprehend the contents of images substantially for the improvement of classification accuracy. However, it also brings in some new problems that how to effectively combine multiple features together, and how to handle the high-dimensional features from multiple views given the small training set. In this paper, we present a large-margin Gaussian process approach to discover the latent space shared by multiple features. Therefore, multiple features can complement each other in this low-dimensional latent space, which derives a strong discriminative ability from the large-margin principle, and then the following classification task can be effectively accomplished. The resulted objective function can be efficiently solved using the gradient descent techniques. Finally, we demonstrate the advantages of the proposed algorithm on real-world image datasets for discovering discriminative latent space and improving the classification performance. © 2013 ACM.
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