Department of Computer Science & Engineering, Manonmaniam Sundaranar University, Tirunelveli, India.
J Med Syst. 2018 Oct 3;42(11):220. doi: 10.1007/s10916-018-1067-6.
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.
特征提取和分类被认为是图像处理应用中的主要任务。本文提出了一种新的方法来提取彩色图像的特征进行分类。所提出的方法，即主导局部纹理-颜色模式（DLTCP），基于 RGB 颜色空间中的主导纹理和主导颜色通道。主导纹理模式表示 RGB 通道中纹理变化最大的通道，而主导颜色模式表示像素强度最大的颜色通道。具有主导纹理模式和主导颜色模式的通道的组合被分配一个唯一的值，用于提取图像的特征。使用来自 Outex 和 Vistex 数据库的彩色图像对所提出的纹理-颜色特征进行旋转、光照和尺度不变性的测试。实验表明，在所提出的方法中，使用 K-最近邻（KNN）分类器在各种挑战下实现了最高的分类精度。
