Extension of higher order local autocorrelation features

Authors:
Takahiro Toyoda;Osamu Hasegawa
Affiliations:
Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, R2-52 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan;Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, R2-52 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan and PRESTO, Japan Science and Technology Agency (JST), Japan
Venue:
Pattern Recognition
Year:
2007

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Abstract

This study investigates effective image features that are widely applicable in image analysis. We specifically address higher order local autocorrelation (HLAC) features, which are used in various applications. The original HLAC features are restricted up to the second order and are represented by 25 mask patterns. We increase their orders up to eight and extract the extended HLAC features using 223 mask patterns. Furthermore, we create large mask patterns and construct multi-resolution features to support large displacement regions. In texture classification and face recognition, the proposed method outperformed Gaussian Markov random fields, Gabor features, and local binary pattern operator.