2005 Volume 34 Issue 4 Pages 390-397
Higher order local autocorrelation (HLAC) features are basic features which are used in various applications. They have been 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 to support large displacement regions. We use masks of different sizes together and construct multi-resolution features. In texture classification, the proposed method outperformed other methods such as Gaussian Markov random fields, Gabor features, and local binary pattern operator. For example, in the classification of small 32 × 32 texture images, the proposed method achieved a 97.5% recognition rate compared to 93.4% using Gabor features. Good performance was also shown for classification of scaled images and 90-degree rotated images, and for classification of numerous classes, over 300, using only a few training samples. The proposed method performed well in face recognition as well. It achieved a 98.4% recognition rate, as opposed to 96.0% using Gabor features. This result indicates its applicability to objects with shape.
