[1]MA Zhongli,LIU Quanyong,WU Lingyu,et al.Syncretic representation method for image classification[J].CAAI Transactions on Intelligent Systems,2018,13(2):220-226.[doi:10.11992/tis.201611036]
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Syncretic representation method for image classification

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 2 Page number: 220-226 Column: 学术论文—机器感知与模式识别 Public date: 2018-04-15
Title:
Syncretic representation method for image classification
Author(s):
MA Zhongli;  LIU Quanyong;  WU Lingyu;  ZHANG Changmao;  WANG Lei
College of Automation, Harbin Engineering University, Harbin 150001, China
Keywords:
image classification; image recognition; syncretic representation; virtual image; pixel intensity; sparse representation; small samples; adjacent columns
CLC:
TP391
DOI:
10.11992/tis.201611036
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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