An unsupervised image segmentation method combining graph clustering and high-level feature representation

Abstract

Image segmentation is one of the most important assignments in computer vision. In this paper, we present an unsupervised segmentation method that combines graph-based clustering and high-level semantic features. We over-segment the given image into a collection of superpixels. Various low-level features assemble a descriptor of each superpixel. Besides the intrinsic image features such as color, texture and gradient, we add image saliency into the low-level visual features as prior knowledge of human perception. Instead of using the low-level features directly, we design a graph-based method to segment the image by clustering the high-level semantic features learned from a neural network. We test the proposed method on two well-known datasets. The experimental evaluation validates that our approach can provide consistent and meaningful segmentation.