Land Cover Mapping with Higher Order Graph-Based Co-Occurrence Model

Land-cover and Land-use Mapping

Higher-order Co-occurrence Features based on Discriminative Co-clusters for Image Classification

Co-occurrence based image features have attracted keen attentions due to the promising performances for image classification tasks [1, 2, 3, 6, 7]. For extracting the co-occurrences, it is common to transform the quantitative data into qualitative data (symbols) by means of quantization (clustering) at first; e.g., continuous gradient orientation is coded into orientation bins [3], RGB colors a...

Clustering Co-occurrence Graph based on Transitivity

Word co-occurrences form a graph, regarding words as nodes and co-occurrence relations as branches. Thus, a co-occurrence graph can be constructed by co-occurrence relations in a corpus. This paper discusses a clustering method of the co-occurrence graph, the decomposition of the graph, from a graph-theoretical viewpoint. Since one of the applications for the clustering results is the ambiguity...

Material for Graph Cut based Inference with Co - occurrence Statistics

1 The Integer Programming formulation, and its Linear Relaxation In this section we show, that the minimisation of the extended energy function E(x) = c∈C ψ c (x c) + C(L(x)) (1) can be formulated as an Integer Program (IP). First the objective function is linearised using a vector z of binary indicator variables to represent the assignment of labels.

Graph Cut Based Inference with Co-occurrence Statistics

Markov and Conditional random fields (CRFs) used in computer vision typically model only local interactions between variables, as this is computationally tractable. In this paper we consider a class of global potentials defined over all variables in the CRF. We show how they can be readily optimised using standard graph cut algorithms at little extra expense compared to a standard pairwise fiel...