CSIFT based locality-constrained linear coding for image classification

被引:1
|
作者
Junzhou Chen
Qing Li
Qiang Peng
Kin Hong Wong
机构
[1] Southwest Jiaotong University,School of Information Science and Technology
[2] The Chinese University of Hong Kong,Department of Computer Science and Engineering
来源
Pattern Analysis and Applications | 2015年 / 18卷
关键词
CSIFT; Sparse coding; LLC; Image classification;
D O I
暂无
中图分类号
学科分类号
摘要
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. locality-constrained linear coding (LLC), a state-of-the-art sparse coding technology, is employed to construct the image-classification system for the evaluation. Moreover, we propose a simple ℓ2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _2$$\end{document}-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 ℓ2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _2$$\end{document}-norm regularization, the proposed image-classification system obtains approximately 2%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2\,\%$$\end{document} improvement of classification accuracy on the Caltech-101 dataset and approximately 5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5\,\%$$\end{document} improvement of classification accuracy on the Caltech-256 dataset.
引用
收藏
页码:441 / 450
页数:9
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