An iterative approach for obtaining multi-scale superpixels based on stochastic graph contraction operations

Abstract

Superpixels have many applications in visual information processing, and can be used to reduce redundant information of an image, as well as the computational complexity of other expensive tasks (e.g., image segmentation). In this work, an iterative hierarchical stochastic graph contraction (IHSGC) method for multi-scale superpixels generation is proposed. A stochastic strategy is used to generate multi-scale superpixels, and each superpixel is represented by a hierarchical tree and describes an image patch at fine and coarse scales simultaneously. The proposed method consists of two main steps. The first step initializes the method based on a multi-channel unsupervised stochastic over-segmentation at the pixel level. The proposed over-segmentation scheme actually performs hierarchical stochastic clustering of visual features (i.e. pixels, image patches, and potentially can be applied to other visual features as well), while preserving the local spatial relationships across different scales. The second step consists of an iterative hierarchical stochastic graph contraction method. Coarser scales are generated by graph contractions until the desired number of superpixels is obtained. The experimental results based on the popular Berkeley segmentation databases BSDS300 and BSDS500 suggest that the proposed approach potentially can perform better than comparative state-of-the-art methods in terms of boundary recall and under-segmentation error.