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 gray-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. localityconstrained linear coding (LLC), a state-of-the-art sparse coding technology, is employed to construct the imageclassification system for the evaluation. Moreover, we propose a simple ' 2 -norm regularized local distance to improve the traditional LLC method. The real experiments are carried out on several benchmarks. With the enhancements to color SIFT and ' 2 -norm regularization, the proposed image-classification system obtains approximately 2 % improvement of classification accuracy on the Caltech-101 dataset and approximately 5 % improvement of classification accuracy on the Caltech-256 dataset.
One of the most outstanding applications is CSIFT REF .
CSIFT based locality-constrained linear coding for image classification
Pattern Analysis and Applications
Pattern Analysis and Applications
Computer Science Mathematics