  In the past decade, SIFT descriptor has been witnessed as one of the most
robust local invariant feature descriptors and widely used in various vision
tasks. Most traditional image classification systems depend on the
luminance-based SIFT descriptors, which only analyze the gray level variations
of the images. Misclassification may happen since their color contents are
ignored. In this article, we concentrate on improving the performance of
existing image classification algorithms by adding color information. To
achieve this purpose, different kinds of colored SIFT descriptors are
introduced and implemented. Locality-constrained Linear Coding (LLC), a
state-of-the-art sparse coding technology, is employed to construct the image
classification system for the evaluation. The real experiments are carried out
on several benchmarks. With the enhancements of color SIFT, the proposed image
classification system obtains approximate 3% improvement of classification
accuracy on the Caltech-101 dataset and approximate 4% improvement of
classification accuracy on the Caltech-256 dataset.
