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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xian-Hua Han Yen-Wei Chen Xiang Ruan |
| Copyright Year | 2010 |
| Description | Author affiliation: Omron corporation, Japan (Xiang Ruan) || College of Information Science and Engineering, Ritsumeikan University, Kusatsu-shi, 525-8577, Japan (Xian-Hua Han; Yen-Wei Chen) |
| Abstract | Image category recognition is important to access visual information on the level of objects and scene types. This paper combines different feature representations of images and learn a compact subspace of different features for the automatic recognition of object and scene classes. Compact visual-words and low-level-features object class subspaces are automatically learned from a set of training images by a Regularized Linear Discriminant analysis (RLDA) algorithm, and the extracted RLDA-domain features are used for Support Vector Machine (SVM) classifier. The main contribution of this paper is two folds: i) Different features (bag-of-features and low-level features)is fused for image representation. ii) The compact feature subspaces (low-dimension features) of different features are learned for rendering to SVM classifier, which is computationally efficient for image category. High classification accuracy is demonstrated on object recognition database (Caltech). We confirm that the proposed strategy cam improve accuracy rate compared with state-of-the-art methods for object recognition databases. |
| Starting Page | 1049 |
| Ending Page | 1052 |
| File Size | 479194 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424479924 |
| ISSN | 15224880 |
| e-ISBN | 9781424479948 |
| e-ISBN | 9781424479931 |
| DOI | 10.1109/ICIP.2010.5653931 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-09-26 |
| Publisher Place | Hong Kong |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Feature extraction Shape Support vector machines Histograms Image color analysis Object recognition Visualization |
| Content Type | Text |
| Resource Type | Article |
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