Local Variation as a Statistical Hypothesis Test
This work provides theoretical justification and improved performance for image segmentation algorithms, which is incremental but beneficial for computer vision researchers and practitioners.
The paper tackled the problem of image oversegmentation by analyzing the local variation algorithm and developing probabilistic variants based on statistical models and hypothesis testing, with the best variant achieving state-of-the-art results while maintaining computational efficiency.
The goal of image oversegmentation is to divide an image into several pieces, each of which should ideally be part of an object. One of the simplest and yet most effective oversegmentation algorithms is known as local variation (LV) (Felzenszwalb and Huttenlocher 2004). In this work, we study this algorithm and show that algorithms similar to LV can be devised by applying different statistical models and decisions, thus providing further theoretical justification and a well-founded explanation for the unexpected high performance of the LV approach. Some of these algorithms are based on statistics of natural images and on a hypothesis testing decision; we denote these algorithms probabilistic local variation (pLV). The best pLV algorithm, which relies on censored estimation, presents state-of-the-art results while keeping the same computational complexity of the LV algorithm.