| 摘要: |
| 针对视觉词袋模型中的特征量化问题,提出一种非负稀疏局部线性编码方法.它能够有效地改善局部特征编码性能,提高图像非线性特征的区分能力.其核心思想是,利用非负稀疏表示技术选择与待编码局部特征处于同一线性空间中的近邻点,然后以这些近邻点作为局部坐标系对当前局部特征进行线性编码.实验结果表明,该局部特征编码方法显著优于现有的特征编码方法,有效地提高了图像非线性特征的区分能力,更有利于图像分类 任务. |
| 关键词: 图像分类 词袋模型 非负稀疏表示 特征量化 |
| DOI： |
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| 基金项目:国家自然科学基金(60933013, 61103134); 国家科技重大专项(2010ZX03004-003); 中央高校基本科研业务经费(WK2100230002, WK2101020003) |
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| Nonnegative Sparse Locally Linear Coding |
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ZHUANG Lian-Sheng, GAO Hao-Yuan, LIU Chao, YU Neng-Hai
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School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China
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| Abstract: |
| Feature quantization is an important component in Bag of word model. This paper proposes a novel method called nonnegative sparse locally linear coding (NSLLC) to improve the performance of locally linear coding. The core ides of NSLLC is to use nonnegative sparse representation to select the nearest neighbors in the same subspace and then encode the local feature with respect to the local coordinate consisting of these nearest neighbors. Experimental results have shown NSLLC has outperformed state-of-the-art local feature coding methods and is in favor of image classification problem. |
| Key words: image classification bag of word nonnegative sparse representation feature quantization |
