Context-Aware and Locality-Constrained Coding for Image Categorization
Improving the coding strategy for BOF (Bag-of-Features) based feature design has drawn increasing attention in recent image categorization works. However, the ambiguity in coding procedure still impedes its further development. In this paper, we introduce a context-aware and locality-constrained Cod...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/632871 |
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| author | Wenhua Xiao Bin Wang Yu Liu Weidong Bao Maojun Zhang |
| author_facet | Wenhua Xiao Bin Wang Yu Liu Weidong Bao Maojun Zhang |
| author_sort | Wenhua Xiao |
| collection | DOAJ |
| description | Improving the coding strategy for BOF (Bag-of-Features) based feature design has drawn increasing attention in recent image categorization works. However, the ambiguity in coding procedure still impedes its further development. In this paper, we introduce a context-aware and locality-constrained Coding (CALC) approach with context information for describing objects in a discriminative way. It is generally achieved by learning a word-to-word cooccurrence prior to imposing context information over locality-constrained coding. Firstly, the local context of each category is evaluated by learning a word-to-word cooccurrence matrix representing the spatial distribution of local features in neighbor region. Then, the learned cooccurrence matrix is used for measuring the context distance between local features and code words. Finally, a coding strategy simultaneously considers locality in feature space and context space, while introducing the weight of feature is proposed. This novel coding strategy not only semantically preserves the information in coding, but also has the ability to alleviate the noise distortion of each class. Extensive experiments on several available datasets (Scene-15, Caltech101, and Caltech256) are conducted to validate the superiority of our algorithm by comparing it with baselines and recent published methods. Experimental results show that our method significantly improves the performance of baselines and achieves comparable and even better performance with the state of the arts. |
| format | Article |
| id | doaj-art-ef97c83ebbca46ebb0452818987e7ca2 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-ef97c83ebbca46ebb0452818987e7ca22025-02-03T07:24:06ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/632871632871Context-Aware and Locality-Constrained Coding for Image CategorizationWenhua Xiao0Bin Wang1Yu Liu2Weidong Bao3Maojun Zhang4College of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, ChinaCollege of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, ChinaCollege of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, ChinaCollege of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, ChinaCollege of Information System and Management, National University of Defense Technology, Changsha, Hunan 410073, ChinaImproving the coding strategy for BOF (Bag-of-Features) based feature design has drawn increasing attention in recent image categorization works. However, the ambiguity in coding procedure still impedes its further development. In this paper, we introduce a context-aware and locality-constrained Coding (CALC) approach with context information for describing objects in a discriminative way. It is generally achieved by learning a word-to-word cooccurrence prior to imposing context information over locality-constrained coding. Firstly, the local context of each category is evaluated by learning a word-to-word cooccurrence matrix representing the spatial distribution of local features in neighbor region. Then, the learned cooccurrence matrix is used for measuring the context distance between local features and code words. Finally, a coding strategy simultaneously considers locality in feature space and context space, while introducing the weight of feature is proposed. This novel coding strategy not only semantically preserves the information in coding, but also has the ability to alleviate the noise distortion of each class. Extensive experiments on several available datasets (Scene-15, Caltech101, and Caltech256) are conducted to validate the superiority of our algorithm by comparing it with baselines and recent published methods. Experimental results show that our method significantly improves the performance of baselines and achieves comparable and even better performance with the state of the arts.http://dx.doi.org/10.1155/2014/632871 |
| spellingShingle | Wenhua Xiao Bin Wang Yu Liu Weidong Bao Maojun Zhang Context-Aware and Locality-Constrained Coding for Image Categorization The Scientific World Journal |
| title | Context-Aware and Locality-Constrained Coding for Image Categorization |
| title_full | Context-Aware and Locality-Constrained Coding for Image Categorization |
| title_fullStr | Context-Aware and Locality-Constrained Coding for Image Categorization |
| title_full_unstemmed | Context-Aware and Locality-Constrained Coding for Image Categorization |
| title_short | Context-Aware and Locality-Constrained Coding for Image Categorization |
| title_sort | context aware and locality constrained coding for image categorization |
| url | http://dx.doi.org/10.1155/2014/632871 |
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