Homogeneous Superpixels from Random Walks
Frank Perbet, Atsuto Maki · 2011
This paper presents a novel algorithm to generate homogeneous superpixels from the process of Markov random walks. We exploit Markov clustering (MCL) as the methodology, a generic graph clustering method based on stochastic flow circulation. In particular, we introduce a new graph pruning strategy called compact pruning in order to capture intrinsic local image structure, and thereby keep the superpixels homogeneous, i.e. uniform in size and compact in shape. Further, this new pruning scheme comes with three advantages: faster computation, smaller memory footprint, and straightforward parallel implementation. Through comparisons with other recent standard techniques, we show that the proposed algorithm achieves an optimal performance in terms of qualitative measure at a decent computational speed. 1