Fast image segmentation based on K-Means clustering with histograms in HSV color space

A fast and efficient approach for color image segmentation is proposed. In this work, a new quantization technique for HSV color space is implemented to generate a color histogram and a gray histogram for K-Means clustering, which operates across different dimensions in HSV color space. Compared with the traditional K-Means clustering, the initialization of centroids and the number of cluster are automatically estimated in the proposed method. In addition, a filter for post-processing is introduced to effectively eliminate small spatial regions. Experiments show that the proposed segmentation algorithm achieves high computational speed, and salient regions of images can be effectively extracted. Moreover, the segmentation results are close to human perceptions.