Qing Li
ABSTRACT
As one of the most robust local invariant feature descriptors, SIFT has been widely used in assorted computer vision and pattern recognition applications. Most traditional image classification systems depend on the gray-based SIFT descriptors, which only calculate the gray layer variations of the images. However, the ignorance of the color information may lead to misclassification of images. In this article, we concentrate primarily on improving the performance of existing image classification algorithms by supplying color information. To accomplish this purpose, various kinds of colored SIFT descriptors were introduced and implemented. Localized soft-assignment coding (LSC), a state-of-the-art sparse coding algorithm, was employed to build a novel image classification system. Real experiments on several benchmarks show that, with the enhancements of color information, the proposed method obtains more than 1% improvement of classification accuracy on the Caltech-101 dataset and approximate 3% improvement of classification accuracy on the Caltech-256 dataset.
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Index Terms
- Application of Localized Soft-Assignment Coding and CSIFT in Image Classification
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