Plain, edge, texture (PET) block classifier using Tchebichef moments and SVM

This paper presents an image block classification method using Tchebichef moments (TMs) and support vector machine (SVM). The test images are divided into non-overlapping 16 × 16 blocks and transformed into moment domain using Discrete Tchebichef Transform. These moment features are then used in the image content (block) classification. SVM is used for learning and classifying the blocks into three types: “plain”, “edge” and “texture”, based on their moment energy level. Experimental results show that the proposed method works well and the classification accuracy is 98.7%.