JSW 2013 Vol.8(5): 1109-1116 ISSN: 1796-217X
doi: 10.4304/jsw.8.5.1109-1116
doi: 10.4304/jsw.8.5.1109-1116
Novel Image Recognition Based on Subspace and SIFT
Tongfeng Sun, Shifei Ding, Zihui Ren
1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
2School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
Abstract—In the light of the deep analyses of subspace recognition and SIFT recognition, a novel image recognition based on subspace and SIFT is proposed to provide a recognition from global features to minutiae features. First, subspace is used to implement coarse image recognition, gaining one or more candidate samples with different identities. Then, a special SIFT recognition environment is designed, in which the approach takes all the images as objects, builds a multi-object sample image with its size limited below a certain size, detects SIFT points based on object regions and recognizes the test image through SIFT point registration statistical vote. The experiments show that the designed SIFT recognition environment can increase SIFT recognition accuracy and the method based on subspace and SIFT can provide accurate recognitions in a mass of images. Under some special environments, recognition accuracy tends to 100%.
Index Terms—Subspace recognition, SIFT recognition, region features, registration statistical vote.
2School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
Abstract—In the light of the deep analyses of subspace recognition and SIFT recognition, a novel image recognition based on subspace and SIFT is proposed to provide a recognition from global features to minutiae features. First, subspace is used to implement coarse image recognition, gaining one or more candidate samples with different identities. Then, a special SIFT recognition environment is designed, in which the approach takes all the images as objects, builds a multi-object sample image with its size limited below a certain size, detects SIFT points based on object regions and recognizes the test image through SIFT point registration statistical vote. The experiments show that the designed SIFT recognition environment can increase SIFT recognition accuracy and the method based on subspace and SIFT can provide accurate recognitions in a mass of images. Under some special environments, recognition accuracy tends to 100%.
Index Terms—Subspace recognition, SIFT recognition, region features, registration statistical vote.
Cite: Tongfeng Sun, Shifei Ding, Zihui Ren, "Novel Image Recognition Based on Subspace and SIFT," Journal of Software vol. 8, no. 5, pp. 1109-1116, 2013.
General Information
ISSN: 1796-217X (Online)
Frequency: Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKI, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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