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NIB2DPCAbased Color Image Feature Extraction Method with Overcomplete Divided

  

  1. 1. School of the Internet of Things Technology, Wuxi Institute of Technology, Wuxi 214121, China;

    2. Engineering Research Center of Internet of Things Applied Technology, Ministry of Education, Jiangnan University, Wuxi 214122, China; 
    3. CoLaboratory in Hillsun Ltd. of Jiangnan University, Wuxi 214122, China
  • Received:2015-10-20 Online:2015-12-23 Published:2015-12-30

Abstract:

Based on color image feature extraction method with small space occupying and fast speed and color image feature extraction method with Modular Factored Principal
Components Analysis(color MFPCA), combined with the latest overcomplete representation idea, a novel method named NIB2DPCAbased color image feature extraction method with
overcomplete divided was proposed. This method can make the color image overcomplete divided into smaller modular images, then NIB2DPCA is employed to extract feature
information from three channels of a given sub color image module respectively. Then the three extracted feature matrices are reconstructed and multi module integrated. Finally,
the classification feature matrix is gotten. The information amount extracted by the novel method is much larger than the original color image by this method and it improves the
recognition accuracy of color image. The results of contrast experiments on CVL and FEI color face databases show that, the proposed method can obtain a higher accuracy by about
4% than color image feature extraction method with small space occupying and fast speed in the reference［14］ and also obtain a higher accuracy by about 5% than color MFPCA
in the reference［19］.

Key words: color face recognition, overcomplete divide, noniteration bilateral two dimensional principal component analysis(NIB2DPCA), feature extraction

CLC Number: