Plotting Color Histograms for RGB Channels

This process involves plotting color histograms for the three RGB channels of color images to extract color features. These features help us better understand the color composition and distribution within images. In implementation, we typically use histogram calculation functions (such as OpenCV's calcHist() or MATLAB's imhist()) to compute the frequency distribution of pixel intensities for each channel, often with 256 bins representing intensity values from 0 to 255. The extracted features can be applied to various computer vision tasks including image classification, object detection, and image retrieval. For image classification, color features serve as discriminative descriptors to differentiate between image categories. In object detection, color characteristics help identify the position and shape of target objects through techniques like color segmentation and thresholding. For image retrieval, color histograms enable similarity measurement between images using distance metrics like Euclidean or Bhattacharyya distance. Thus, color features hold significant application value in computer vision, with common implementations involving histogram equalization, color space conversion, and feature vector normalization for improved performance.