Homogeneous Superpixels from Markov Random Walks

Homogeneous Superpixels from Markov Random Walks

This paper presents a novel algorithm to generate homogeneous superpixels from 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 graph pruning strategy called compact pruning in order to capture intrinsic local image structure. The resulting superpixels are homogeneous...

Homogeneous Superpixels from Random Walks

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 s...

Random walks and Markov chains

This lecture discusses Markov chains, which capture and formalize the idea of a memoryless random walk on a finite number of states, and which have wide applicability as a statistical model of many phenomena. Markov chains are postulated to have a set of possible states, and to transition randomly from one state to a next state, where the probability of transitioning to a particular next state ...

Markov Chains and Random Walks

Let G = (V,E) be a connected, undirected graph with n vertices and m edges. For a vertex v ∈ V , Γ(v) denotes the set of neighbors of v in G. A random walk on G is the following process, which occurs in a sequence of discrete steps: starting at a vertex v0, we proceed at the first step to a random edge incident on v0 and walking along it to a vertex v1, and so on. ”Random chosen neighbor” will ...

Zariski Dense Random Walks on Homogeneous Spaces