可见光-近红外HSV图像融合的场景类字典稀疏识别方法

doi:10.11772/j.issn.1001-9081.2018040806

计算机应用 ›› 2018, Vol. 38 ›› Issue (12): 3355-3359.DOI: 10.11772/j.issn.1001-9081.2018040806

• 人工智能 • 下一篇

可见光-近红外HSV图像融合的场景类字典稀疏识别方法

刘佶鑫^1,2, 魏嫚²

1. 宽带无线通信技术教育部工程研究中心(南京邮电大学), 南京 210003;
2. 南京邮电大学通信与信息工程学院, 南京 210003

收稿日期:2018-04-19 修回日期:2018-06-05 出版日期:2018-12-10 发布日期:2018-12-15
通讯作者: 刘佶鑫
作者简介:刘佶鑫(1982-),男,江苏南京人,副教授,博士,主要研究方向:压缩感知、图像处理和识别;魏嫚(1994-),女,江苏徐州人,硕士研究生,主要研究方向:图像识别、信息处理。
基金资助:
国家自然科学基金资助项目（61401220，61471206）；南京邮电大学科研基金资助项目（NY218066）。

Scene sparse recognition method via intra-class dictionary for visible and near-infrared HSV image fusion

LIU Jixin^1,2, WEI Man²

1. Engineering Research Center of Wideband Wireless Communication Technology, Ministry of Education, (Nanjing University of Posts and Telecommunications), Nanjing Jiangsu 210003, China;
2. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing Jiangsu 210003, China

Received:2018-04-19 Revised:2018-06-05 Online:2018-12-10 Published:2018-12-15
Contact: 刘佶鑫
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61401220, 61471206), the Scientific Research Foundation of Nanjing University of Posts and Telecommunications (NY218066).

摘要/Abstract

摘要： 针对典型自然场景智能观测的需求，为提高稀疏分类器在小样本数据库上的识别精度，提出一种可见光和近红外（NIR）HSV图像融合的场景类字典稀疏识别方法。首先，利用一直应用在计算机视觉显示领域中的图像HSV伪彩色处理技术将近红外图像与可见光图像融合；然后，对融合图像进行通用搜索树（GiST）特征和分层梯度方向直方图（PHOG）特征的提取与融合；最后，结合提出的类字典稀疏识别方法得到场景分类结果。所提方法在RGB-NIR数据库上的实验识别精度达到了74.75%。实验结果表明，融合近红外信息的场景图像的识别精度高于未融合时的识别精度，所提方法能够有效增加稀疏识别框架下场景目标的信息表征质量。

关键词: 图像融合, 近红外, 可见光, 场景识别, 稀疏识别

Abstract: Focusing on the requirement of intelligent observation of typical natural scenes, in order to improve the recognition accuracy of sparse classifiers in small sample databases, a new scene sparse recognition method via intra-class dictionary for visible and Near-InfraRed (NIR) HSV (Hue, Saturation, Value) image fusion was proposed. Firstly, the near infrared image and the visible image were fused by HSV pseudo-color processing technology which had been used in the field of computer vision display. Then, the global Generalized Search Tree (GiST) features and local Pyramid Histogram of Oriented Gradients (PHOG) features were extracted and fused. Finally, the scene classification results were obtained by combining the proposed dictionary-like sparse recognition method. The experimental recognition accuracy of the proposed method on RGB-NIR database is 74.75%. The experimental results show that, the recognition accuracy of scene images fused with near infrared information is higher than that of non-fused images. The proposed method can effectively improve the information representation quality of scene targets in sparse recognition framework.

Key words: image fusion, Near-InfraRed (NIR), visible light, scene recognition, sparse recognition

中图分类号:

TP391.41

刘佶鑫, 魏嫚. 可见光-近红外HSV图像融合的场景类字典稀疏识别方法[J]. 计算机应用, 2018, 38(12): 3355-3359.

LIU Jixin, WEI Man. Scene sparse recognition method via intra-class dictionary for visible and near-infrared HSV image fusion[J]. Journal of Computer Applications, 2018, 38(12): 3355-3359.

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可见光-近红外HSV图像融合的场景类字典稀疏识别方法

Scene sparse recognition method via intra-class dictionary for visible and near-infrared HSV image fusion

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