Research on Application of Sparse Representation in Feather and down Category Recognition

Abstract:

In signal analysis, as a new representation, the sparse representation caused widespread concern of scholars at home and abroad, and signal processing and analysis produced a very significant impact. Feather and Down is closely related to people's lives, different kinds of down, the price difference is bigger, and thermal properties are different, and therefore, feather species identification has always been an important issue. This paper studies the sparse representation in image processing, while the types of detection of Down key technologies studied, proposed a new algorithm to detect the type of feather sparse representation. In this algorithm, an improved sparse we will de-noising method based on the introduction of a global atomic library come down on the previous species detection algorithm has been improved. The algorithm is applied to obtain a certain effect, making feather and down recognition rate has improved to some extent, the effect is significant.

• Periodical: Advanced Materials Research (Volumes 1049-1050)
• Pages: 1297-1301
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1049-1050.1297
• Online since: October 2014
• Authors: Xiao Fei Yan*, Yan Qiu Wang
• Keywords: Down Category, Feather Category, Image Recognition, Recognition Rate, Sparse Representation

References

[1] Ge Hongwei, Yan Xiaofei, A Feather and down Recognition System Based on SVM[J], Computer Engineering and Application, 2005. 9, 41(27): 221-223.

[2] Yan Xiaofei; Ge Hongwei, Application of SVM in Feather and Down Recognition, Computer Application and Software, 2005. 9: 99-101.

[3] Yanqiu Wang, A Revised Feather and Down Recognition Model Based on MOAA SVM, In Proceedings of ICIME 2010, pp.482-485, (2010).

DOI: 10.1109/icime.2010.5478119

[4] Xiaofei Yan, Yanqiu Wang, A Feather and Down Category Recognition System Based on SVM and GA, In Proceedings of ICETC 2009, p.128–132, (2009).

DOI: 10.1109/icetc.2009.50

[5] Yan Xiaofei; Ge Hongwei, Research of SVM and it's Application, Computer Engineering and Design, 2006. 6, 27(11): 1996-(1997).

[6] Shi Fankui, Application Research of SVM for Small-set Off-line Handwritten Chinese characters Recognition, Computer Engineering, 2002. 6, 28(6): 154-155.