Local Variation as a Statistical Hypothesis Test

Michael Baltaxe(1), Peter Meer(2) and Michael Lindenbaum(3)

Orbotech Ltd., Yavne 8110101, Israel

(2)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08855, USA

Computer Science Department, Technion
Israel Institute of Technology, Haifa 32000, Israel.


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 ective 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 dirent statistical models and decisions, thus providing further theoretical justi cation and a wellfounded 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.

International Journal of Computer Vision, 117, 131--141, 2016.
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