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| Image Categorization Algorithm Based on Improved Sparse Coding Model |
| TANG Feng-1, SUN Tan-Feng-1, 2 , JIANG Xing-Hao-1, 2 , LU Huan-1 |
| (1. School of Electronic, Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China; 2. Shanghai Information Security Management and Technology Research Key Laboratory, Shanghai 200240, China) |
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Abstract A novel image categorization algorithm based on improved sparse coding model for image representation and Random Forests for image classification was proposed. Firstly SIFT (ScaleInvariant Feature Transform) descriptors were extracted from images. Then sparse coding was adopted to train a visual dictionary and convert SIFT descriptors into sparse vectors. An efficient pooling method was employed to merge the sparse vectors in each grid of image and a pooled sparse vector was formed to represent the grid. According to the position of grids, the pooled sparse vectors were combined to form a single sparse vector for representing an image. Secondly Random Forests, a multiclass classifier was employed to classify sparse vectors which represent images. The experimental results show that the algorithm outperforms several stateofthearts in image categorization.
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Received: 14 October 2011
Published: 28 September 2012
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