Texture Classification Using Extended Higher Order Local Autocorrelation Features
Takahiro Toyoda, Osamu Hasegawa · 2005
This study investigates effective image features for characterization of local regions. We propose an extension of higher order local autocorrelation (HLAC) features. The original HLAC features are restricted up to the second order. They are represented by 25 mask patterns. We increase their orders up to eight and extract the extended HLAC features using 223 mask patterns. Large mask patterns are also created to support large displacement regions. They are used to construct multi-resolution HLAC features. The proposed method outperforms Gaussian Markov random fields, Gabor features, and local binary pattern operator in texture classification. 1