A general framework for reconciling multiple weak segmentations of an image

Segmentation, or partitioning images into internally homogeneous regions, is an important first step in many computer vision tasks. In this paper, we attack the segmentation problem using an ensemble of low cost image segmentations. These segmentations are reconciled by applying recent techniques from the consensus clustering literature which exploit a non-negative matrix factorization (NMF) framework. We describe extensions to these methods that scale them for large images and also incorporate smoothness constraints. This framework allows us to uniformly and easily combine segmentations from different algorithms or feature modalities. We then demonstrate that popular bottom up image segmentation algorithms, Mean shift and efficient graph based segmentation, perform no better than our simple combination of multiple image segmentations derived from k-means clustering (of various feature spaces) or from ¿naive¿ RGB quantizations. The algorithms are evaluated on the Berkeley image segmentation dataset.