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According to the nature of saliency map generation with colour contrast, a non-attention region first initialisation (NARFI) k-means clustering for saliency detection is proposed. The NAR is obtained by multiwavelet reconstruction based on the cutoff low-frequency. The initial seeds of the k-means are chosen from the NAR. This way, the NAR is clustered in a fine manner, whereas the attention region is clustered in a coarse manner. As a result, the saliency values of the attention region with the NARFI k-means clustering are more conspicuous than those with the k-means++ clustering.

On the base of edge detection using the K-means algorithm and an improved ant colony optimization (ACO), a novel image segmentation algorithm is proposed. The proposed method can enhance advantages and avoid disadvantages of edge-based and clustering methods by embedding the clustering in edge detection, combining them in a novel way. Since the clustering centers are determined roughly using K-means in ACO, the proposed algorithm can address the problem of slow convergence of the traditional ant colony (AC) algorithm and reduces its complexity. Experiments are divided into two stages to test the results of edge detection and segmentation respectively. It is shown that the proposed algorithm based on superior edge detection achieves better performance compared to the typical image segmentation methods.