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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zheng, Yingbin Xue, Xiangyang Lu, Hong Wei, Renzhong |
| Abstract | Local descriptor extraction and vector quantization are the important components of widely-used Bag-of-Features (BoF) model for visual categorization. This paper proposes a simple and efficient approach to refine the local descriptors for vector quantization by embedding semantic information. The original local descriptors are integrated by a sequence of category-independent and category-dependent basis. Particularly, the category-dependent basis is learned by minimizing the joint loss minimization over local descriptors from different categories with a shared regularization penalty, which can be formulated as a linear programming problem. The transferred descriptors are further quantized and aggregated to the visual vocabulary. Experiments are performed on PASCAL VOC 2007 benchmark and the quantitative comparisons with several state-of-the-art approaches demonstrate the effectiveness of our proposed approach. |
| Starting Page | 1381 |
| Ending Page | 1384 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450306164 |
| DOI | 10.1145/2072298.2072020 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-11-28 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Semantic information Visual categorization Local descriptor |
| Content Type | Text |
| Resource Type | Article |
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