Color Feature Extraction Algorithm for Color Images
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This paper presents a detailed discussion of color feature extraction algorithms for color images based on an IEEE CSVT 2001 publication focusing on color histogram methods. The authors introduce a novel algorithm that extracts color features by analyzing pixel value distributions within images, enabling accurate description and analysis of color images. The implementation typically involves creating histogram bins for different color channels (RGB/HSV), calculating frequency distributions using functions like numpy.histogram(), and normalizing the results for scale invariance. This research holds significant importance for image processing and computer vision fields, providing valuable references and foundations for further studies and applications. The algorithm's key functions include color space conversion, bin allocation strategies, and distance metrics for histogram comparison, making it particularly useful for content-based image retrieval systems.
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