Structural Image Segmentation with Interactive Model Generation

Abstract

An image segmentation method based on structural pattern recognition is presented. Two graphs are generated from the image to be segmented. A model graph is generated from an oversegmentation of the image and from traces provided by the user. An input graph is generated from the oversegmented image. Image segmentation is then obtained by matching the input graph to the model graph. An objective function is defined and optimized using a new approach to find the most suitable clique of the corresponding association graph. The structural information encoded in the graphs leads to a robust segmentation performance even in the case of non-homogeneous textured regions. Successful experimental results obtained from real images are provided.

A Computer-Assisted Colorization Approach Based on Efficient Belief Propagation and Graph Matching

“…The edges dissimilarities described in [5] were used as a Markov component, leading to a very useful tool for point matching problems. The key to achieve efficiency was the assumption that important contextual information is concentrated on close neighbors.…”

“…Two vertices p ∈ V , q ∈ V are adjacent if (p, q) ∈ E. µ assigns an attribute vector to each vertex of V . Similarly, ν assigns an attribute vector to each edge of E. Following the same notation used in [5], we focus on matching two graphs, an input graph G i , representing the scene (input image) with all patterns to be classified, and a model graph G m , representing the template with all classes. Given two ARGs,…”

“…The Markov component compares edge attributes through the dissimilarity function defined by Equation 8 [5], which compares pairs of vectors in terms of angle and lengths in order to characterize the spatial relations.…”

“…Here, we propose a simple approach based on area, contour length and point locations used to encode the spatial relations as described in [5] and [8]. The objetive of the proposed general framework based on MRF is to overcome the main difficulty in graph matching problems, expressed by the following question.…”

Structural Matching of 2D Electrophoresis Gels using Graph Models

“…In order to evaluate the structural distance, each input image is represented by a graph, where each 2D point is assigned to a vertex, and edges are created in order to represent structural relations between vertices. The proposed method is inspired by the graph matching approach for image segmentation described in [5,6,7].…”