[1]马忠丽,刘权勇,武凌羽,等.一种基于联合表示的图像分类方法[J].智能系统学报,2018,13(02):220-226.[doi:10.11992/tis.201611036]
 MA Zhongli,LIU Quanyong,WU Lingyu,et al.Syncretic representation method for image classification[J].CAAI Transactions on Intelligent Systems,2018,13(02):220-226.[doi:10.11992/tis.201611036]
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一种基于联合表示的图像分类方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年02期
页码:
220-226
栏目:
出版日期:
2018-04-15

文章信息/Info

Title:
Syncretic representation method for image classification
作者:
马忠丽 刘权勇 武凌羽 张长毛 王雷
哈尔滨工程大学 自动化学院, 黑龙江 哈尔滨 150001
Author(s):
MA Zhongli LIU Quanyong WU Lingyu ZHANG Changmao WANG Lei
College of Automation, Harbin Engineering University, Harbin 150001, China
关键词:
图像分类图像识别联合表示虚拟图像像素强度稀疏表示小样本相邻列
Keywords:
image classificationimage recognitionsyncretic representationvirtual imagepixel intensitysparse representationsmall samplesadjacent columns
分类号:
TP391
DOI:
10.11992/tis.201611036
摘要:
在图像分类识别中,对于同一目标的不同图像,其训练样本和测试样本在同一位置的像素强度通常不同,这不利于提取目标图像的显著特征。这里给出一种基于稀疏表示的联合表示的图像分类方法,此方法首先利用相邻列之间的关系得到原始图像对应的虚拟图像,利用虚拟图像提高图像中中等强度像素的作用,降低过大或过小强度像素对图像分类的影响;然后用同一个目标的原始图像和虚拟图像一起表示目标,得到目标图像的联合表示;最后利用联合表示方法对目标分类。针对不同目标图像库的实验研究表明,给出的联合方法优于利用单一图像进行分类的方法,而且本方法能联合不同的表示方法来提高图像分类正确率。
Abstract:
In the classified recognition of image, for different images of the same object, because the pixel intensities of the training samples and the test samples at the same positions are usually different, it brings difficulty to extracting the salient features of the object images. This paper proposed an image classification method based on syncretic representation of the sparse representation. Firstly, the virtual image of an original image is obtained by using the connection between adjacent columns of the original image, the virtual image is utilized to enhance the importance of the pixel with moderate intensity and reduce the effects of the pixels with overlarge or undersize intensity on image classification; then the original image and virtual image of the same object are together used to represent the object, so as to obtain the syncretic representation of an object image; finally, the syncretic representation method is used for object classification. The experiments on different object image libraries show that, the given syncretic method is superior to the classification method realized by utilizing single image, in addition, by combining the method with other different representation methods, the accuracy of image classification can be improved.

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备注/Memo

备注/Memo:
收稿日期:2016-11-30。
基金项目:黑龙江省自然科学基金项目(LC201425).
作者简介:马忠丽,女,1974年生,副教授,博士,主要研究方向为机器视觉、模式识别与机器人技术。主持和参与完成国家自然科学基金项目、黑龙江省自然科学基金项目多项,发表学术论文20余篇;刘权勇,男,1990年生,硕士研究生,主要研究方向为图像分类和人脸识别。
通讯作者:刘权勇.E-mail:690175562@qq.com.
更新日期/Last Update: 1900-01-01