Image Feature Extraction by Sparse Coding and Independent Component Analysis
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Palmprint recognition method based on a new kernel sparse representation method
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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A novel image reconstruction method for natural images using a modified sparse coding (SC) algorithm is proposed by us. This SC algorithm exploited the maximum Kurtosis as the maximizing sparse measure criterion, and a fixed variance term of sparse coefficients is used to yield a fixed information capacity. The experimental results show that using our algorithm, the natural images' feature basis vectors can be successfully extracted. Furthermore, compared with the standard SC method, the experimental results show that our algorithm is indeed efficient and effective in performing image reconstruction task.