Local Image Descriptors Using Supervised Kernel ICA

  • Masaki Yamazaki
  • Sidney Fels
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)


PCA-SIFT is an extension to SIFT which aims to reduce SIFT’s high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminative representation for recognition due to its global feature nature and unsupervised algorithm. In addition, linear methods such as PCA and ICA can fail in the case of non-linearity. In this paper, we propose a new discriminative method called Supervised Kernel ICA (SKICA) that uses a non-linear kernel approach combined with Supervised ICA-based local image descriptors. Our approach blends the advantages of a low dimensionality representation, like PCA-SIFT, with supervised learning based on non-linear properties of kernels to overcome separability limitations of nonlinear representations for recognition. Using five different test data sets we show that the SKICA descriptors produce better object recognition performance than other related approaches with the same dimensionality. The SKICA-based representation has local sensitivity, non-linear independence and high class separability providing an effective method for local image descriptors.


Principal Component Analysis Object Recognition Linear Discriminant Analysis Independent Component Analysis Interest Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Masaki Yamazaki
    • 1
  • Sidney Fels
    • 2
  1. 1.Faculty of Information Science and EngineeringRitsumeikan UniversityShigaJapan
  2. 2.Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouverCanada

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