Segmentation Using Superpixels: A Bipartite Graph Partitioning Approach

This paper presents a superpixel-based bipartite graph partitioning approach to address image segmentation challenges. The core innovation involves transforming traditional pixel-level graph partitioning into superpixel-level bipartite graph segmentation, significantly improving computational efficiency. The algorithm operates in two primary stages: First, the superpixel generation phase employs clustering algorithms (typically using k-means or SLIC method implementations) to aggregate adjacent pixels with similar color/texture characteristics into superpixel blocks. This preprocessing step dramatically reduces the number of graph nodes for subsequent processing. In code implementation, this often involves calculating color similarity metrics and spatial proximity using Euclidean distance in CIELAB color space. Second, the bipartite graph construction phase reformulates the image segmentation problem as a bipartite graph partitioning task. One partition contains superpixel nodes while the other corresponds to abstract region labels. By defining appropriate edge weights (incorporating features like color similarity, spatial distance, and texture coherence) in the bipartite graph, the segmentation problem transforms into a graph partitioning optimization problem. Implementation-wise, this requires constructing affinity matrices where weights between superpixels and labels reflect their probabilistic associations. Key methodological innovations include: - Utilizing superpixels as intermediate representations to balance precision and computational efficiency - Designing specialized bipartite graph structures that provide mathematical rigor for region merging operations - Formulating graph partitioning objective functions that preserve natural region boundaries through normalized cut criteria Compared to conventional methods, this superpixel-based bipartite graph partitioning approach maintains segmentation quality while substantially reducing computational complexity, particularly beneficial for high-resolution image processing. Experimental results presented at CVPR 2012 demonstrated superior performance over contemporary state-of-the-art methods on standard benchmark datasets, with implementations typically achieving O(n log n) complexity through efficient graph cut algorithms.