Abstract
In this paper, we apply the sparse representation based algorithm to the problem of generic image classification. Keypoints with different descriptors are used as the bases of the training matrix and test samples. A learning algorithm is also presented to select the most important keypoints as the bases of the training matrix. Experiments have been done on 25 object categories selected from Caltech101 dataset, with salient region detector and different descriptors. The results show that keypoints with histogram of oriented gradients descriptor can achieve good performance on image categories which have distinctive patterns detected as keypoints. Furthermore, the base learning algorithm is useful for improving the performance while reducing the computational complexity.
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Zuo, Y., Zhang, B. (2011). Sparse Based Image Classification with Different Keypoints Descriptors. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21090-7_38
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DOI: https://doi.org/10.1007/978-3-642-21090-7_38
Publisher Name: Springer, Berlin, Heidelberg
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