Entropy Rate Superpixel Segmentation

Application Background Superpixel segmentation constitutes a fundamental module for various computer vision applications such as object recognition, image segmentation, and single-view 3D reconstruction. Technically, a superpixel refers to a perceptually homogeneous region in an image. Key Technology We formulate the superpixel segmentation problem as an optimization task on graph topology. Our novel approach introduces an objective function based on the entropy rate of random walks on graphs, which can be implemented using Markov chain computations and probability transition matrices. The derived algorithm achieves provable optimality bounds with efficient computational performance, typically involving graph partitioning techniques and iterative optimization methods. For future work, we plan to investigate the applicability of this model to general clustering problems and conduct further experimental validation across diverse datasets. Implementation Insight: The core algorithm involves constructing affinity graphs from image pixels, calculating transition probabilities, and optimizing the entropy-based objective function through iterative clustering updates. Key functions would include graph construction, entropy calculation, and cluster assignment operations.