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Proceedings Paper

Super pixel density based clustering automatic image classification method
Author(s): Mingxing Xu; Chuan Zhang; Tianxu Zhang
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Paper Abstract

The image classification is an important means of image segmentation and data mining, how to achieve rapid automated image classification has been the focus of research. In this paper, based on the super pixel density of cluster centers algorithm for automatic image classification and identify outlier. The use of the image pixel location coordinates and gray value computing density and distance, to achieve automatic image classification and outlier extraction. Due to the increased pixel dramatically increase the computational complexity, consider the method of ultra-pixel image preprocessing, divided into a small number of super-pixel sub-blocks after the density and distance calculations, while the design of a normalized density and distance discrimination law, to achieve automatic classification and clustering center selection, whereby the image automatically classify and identify outlier. After a lot of experiments, our method does not require human intervention, can automatically categorize images computing speed than the density clustering algorithm, the image can be effectively automated classification and outlier extraction.

Paper Details

Date Published: 14 December 2015
PDF: 7 pages
Proc. SPIE 9812, MIPPR 2015: Automatic Target Recognition and Navigation, 98120Z (14 December 2015); doi: 10.1117/12.2208985
Show Author Affiliations
Mingxing Xu, Huazhong Univ. of Science and Technology (China)
Chuan Zhang, Huazhong Univ. of Science and Technology (China)
Tianxu Zhang, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 9812:
MIPPR 2015: Automatic Target Recognition and Navigation
Nong Sang; Xinjian Chen, Editor(s)

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