A novel image distance based on Gabor feature and approximated manifold

作者：Zhou Hua^*; Ding Mingyue; Cai Chao

来源：7th Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Pattern Recognition and Computer Vision, 2011-11-04 to 2011-11-06.

DOI：10.1117/12.902075

• Gabor transform
• manifold distance
• manifold learning
• Maximum Variance Unfolding
• Taylor-expansion

Tangent distance method approximates nonlinear manifolds by their tangent hyperplanes and has been widely used in image recognition. However tangent distance directly deals with original images while high-order statistic information may be neglected. And the information of image transformation should be known a priori. We propose a new image distance metric-Gabor feature-based approximated manifold distance (GFMD) to address these disadvantages. Firstly Gabor wavelet transform are applied to calculate high-order statistical information of images. The intrinsic variables of feature manifold are revealed by MVU. The feature manifold can be approximated by curve surfaces based on second-order Taylor expansion. GFMD is defined as the minimum distance between the approximated curved surfaces and can be directly combined with distance-based classifiers for image recognition. The experimental results of face recognition demonstrate that GFMD not only has higher invariance of transformation but also has more stability of classification than several state-of-the-art distance metrics.

• 出版日期2011
• 单位华中科技大学