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| Image Classification Using Multiple Kernel Learning and Sparse Coding |
| CHENG Dong-Yang, JIANG Xing-Hao, SUN Tan-Feng |
| (School of Electronic, Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China) |
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Abstract An image classification algorithm based on sparse coding and multiple kernel learning (MKL) was proposed. First, D-SIFT (Dense Scale-Invariant Feature Transform) and D-SURF (Dense Speeded Up Robust Feature) are extracted from images. Then, sparse coding method is adopted to represent an image as a vector and max pooling method is also utilized for both features. Finally, an improved MKL is used to classify those vectors. Appropriate kernel combinations are selected for each feature and the final result is the fusion of both. The experiments demonstrate that the algorithm remarkably improves the classification accuracy.
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Received: 12 February 2012
Published: 30 November 2012
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