Feature Extraction Using Dominant Local Texture-Color Patterns (DLTCP) and Classification of Color Images

Feature extraction and classification are considered to be the major tasks in image processing applications. This paper proposes a novel method to extract the features of a color image for classification. The proposed method, Dominant Local Texture-Color Patterns (DLTCP) is based on the Dominant Texture and Dominant Color channels in a RGB color space. The dominant texture pattern represents a channel among RGB with maximum variations in the texture and the dominant color pattern represents the color channel with the maximum pixel intensity. The combination of channels with dominant texture pattern and dominant color pattern is assigned a unique value which is used to extract the features of an image. The proposed texture-color features is tested for rotational, illumination and scale invariance property using the color images taken from Outex and Vistex databases. It is experimentally shown that the proposed method achieves the highest accuracy in classification using K-Nearest Neighbor (KNN) classifier under various challenges.