29 May 2013 Decoupling sparse coding of SIFT descriptors for large-scale visual recognition
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Abstract
In recent years, sparse coding has drawn considerable research attention in developing feature representations for visual recognition problems. In this paper, we devise sparse coding algorithms to learn a dictionary of basis functions from Scale- Invariant Feature Transform (SIFT) descriptors extracted from images. The learned dictionary is used to code SIFT-based inputs for the feature representation that is further pooled via spatial pyramid matching kernels and fed into a Support Vector Machine (SVM) for object classification on the large-scale ImageNet dataset. We investigate the advantage of SIFT-based sparse coding approach by combining different dictionary learning and sparse representation algorithms. Our results also include favorable performance on different subsets of the ImageNet database.
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Zhengping Ji, James Theiler, Rick Chartrand, Garrett Kenyon, Steven P. Brumby, "Decoupling sparse coding of SIFT descriptors for large-scale visual recognition", Proc. SPIE 8750, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 87500K (29 May 2013); doi: 10.1117/12.2018204; https://doi.org/10.1117/12.2018204
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