.png){kind=icon}
返回
A robust local sparse coding method for image classification with Histogram Intersection Kernel
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2015.07.136.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Local sparse coding methods have been shown to lead to increased performance in image classification when it takes histograms as inputs. These methods often use Euclidean (l(2)) distance to learn the dictionary and encode the histograms. However, it has been shown that Histogram Intersection Kernel (HIK) is more effective to compare histograms. In this paper, we combine Histogram Intersection Kernel with local sparse coding. We implement dictionary learning and feature encoding on the mapping space that corresponds to the kernel. To encode the features, we propose two methods: one accurate method generating codes consisting of positive and negative values and one approximate method generating only non-negative values. Both of the two encoding methods run very fast. To verify our method, we conduct some experiments on two popular datasets: Caltech-101 and Caltech-256. The results show that the features extracted by our method are more discriminative than other methods and it reaches state-of-the-art result on Caltech-101 when taking single descriptor HOG as input In addition, it shows that the codes with non-negative constraint are more effective than that without the constraint. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Local sparse coding
Histogram Intersection Kernel
Image classification
Non-negative sparse coding
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
.png){kind=icon}
查看更多内容