| Tian Dongping (田东平),Zhao Xiaofei,Shi Zhongzhi.[J].高技术通讯(英文),2013,19(3):295~300 |
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| Support vector machine with mixture of kernels for automatic image annotation |
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| DOI： |
| 中文关键词: |
| 英文关键词: automatic image annotation (AIA), support vector machine (SVM), kernel function, principal component analysis (PCA) |
| 基金项目: |
| Author Name | Affiliation | | Tian Dongping (田东平) | | | Zhao Xiaofei | | | Shi Zhongzhi | |
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| 中文摘要: |
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| 英文摘要: |
| Automatic image annotation (AIA) has become an important and challenging problem in computer vision due to the existence of semantic gap. In this paper, a novel support vector machine with mixture of kernels (SVM-MK) for automatic image annotation is proposed. On one hand, the combined global and local block-based image features are extracted in order to reflect the intrinsic content of images as complete as possible. On the other hand, SVM-MK is constructed to shoot for better annotating performance. Experimental results on Corel dataset show that the proposed image feature representation method as well as automatic image annotation classifier, SVM-MK, can achieve higher annotating accuracy than SVM with any single kernel and mi-SVM for semantic image annotation. |
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