Content-sensitive superpixel segmentation via self-organization-map neural network

Content-sensitive superpixel segmentation generates small superpixels in content-dense regions and large superpixels in content-sparse regions. It achieves higher segmentation accuracy than traditional superpixels. In this paper, we propose a content-sensitive superpixel segmentation algorithm based on Self-Organization-Map (SOM) neural network. First, we propose a novel metric to measure the content-sensitiveness of superpixels. Second, by using this metric, we develop a sampling algorithm to sample pixels from image according to their content-sensitiveness. Finally, a SOM neutral network is trained with the sampled pixels and used to segment the image into content-sensitive superpixels. The Berkeley Image Segmentation database and INRIA database are used to evaluate the proposed method. The experiment results show that the proposed approach outperforms state-of-the-art methods.