Local Image Descriptors Using Supervised Kernel ICA

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概要

• 論文の詳細を見る

• PCA-SIFT is an extension to SIFT which aims to reduce SIFTs 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 supervised learning with nonlinear properties of kernels. 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.

• 2009-09-01

著者

• Yamazaki Masaki

  Graduate School Of Science And Engineering Ritsumeikan University

• Fels Sidney

  Department Of Electrical And Computer Engineering University Of British Columbia

• Fels Sidney

  Department Of Electrical And Computer Engineering The University Of British Columbia

関連論文

• Local Image Descriptors Using Supervised Kernel ICA
• AN EDGE DETECTION METHOD FOR THREE-DIMENSIONAL GRAY IMAGES USING OPTIMUM SURFACE FITTING(International Workshop on Advanced Image Technology 2006)

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