空间颜色聚类算法及其在图像特征提取中的应用

Spatial Color Clustering Algorithm and Its Application in Image Feature Extraction

针对目前图像基本颜色特征单一、可提取信息不足的问题,提出将图像进行HSV颜色空间转换,先用均衡化直方图增强空间颜色互转后的图像空间颜色像素,再用融入rgb2ind的K-means均值聚类算法提亮均衡图像,以增强图像颜色特征的提取目标和数量.实验结果表明,该方法优于普通空间颜色图像进行特征提取的效果,实现了为颜色特征识别提供更多的检索信息.

Aiming at the current problem of single basic color features of image and insufficient information available, we proposed to transform the image into HSV color space. We first used the equalization histogram to enhance the spatial color pixels of the image after the spatial color conversion, and then used K-means clustering algorithm integrated with rgb2 ind to brighten the balanced image, so as to enhance the extraction target and quantity of image color feature. The experimental results show that the proposed method is better than that of the ordinary spatial color image for feature extraction, and provides more retrieval information for color feature recognition.