Color image segmentation using an enhanced Gradient Network Method

Authors:
A. V. Wangenheim;R. F. Bertoldi;D. D. Abdala;A. Sobieranski;L. Coser;X. Jiang;M. M. Richter;L. Priese;F. Schmitt
Affiliations:
Image Proc. and Computer Graphics Lab - LAPIX, Federal University of Santa Catarina, Florianópolis, Brazil;Image Proc. and Computer Graphics Lab - LAPIX, Federal University of Santa Catarina, Florianópolis, Brazil;CS Department, University of Münster, Münster, Germany;Image Proc. and Computer Graphics Lab - LAPIX, Federal University of Santa Catarina, Florianópolis, Brazil;Image Proc. and Computer Graphics Lab - LAPIX, Federal University of Santa Catarina, Florianópolis, Brazil;CS Department, University of Münster, Münster, Germany;CS Department, University of Calgary, Alberta, Canada;CS Department, University of Koblenz, Koblenz, Germany;CS Department, University of Koblenz, Koblenz, Germany
Venue:
Pattern Recognition Letters
Year:
2009

Citing 16
Cited 4

A physical approach to color image understanding

International Journal of Computer Vision
Distance-based functions for image comparison

Pattern Recognition Letters
Structured spatial domain image and data comparison metrics

VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and Robust Segmentation of Natural Color Scenes

ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
An empirical approach to grouping and segmentation

An empirical approach to grouping and segmentation
Graph Partitioning Active Contours (GPAC) for Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Objective Evaluation of Image Segmentation Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Color image segmentation guided by a color gradient network

Pattern Recognition Letters
Combining local belief from low-level primitives for perceptual grouping

Pattern Recognition
Image segmentation with ratio cut

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale methods for the segmentation and reconstruction of signals and images

IEEE Transactions on Image Processing

Learning a nonlinear distance metric for supervised region-merging image segmentation

Computer Vision and Image Understanding
Color histogram-based image segmentation

CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
A fuzzy clustering algorithm with spatial robust estimation constraint for noisy color image segmentation

Pattern Recognition Letters
Multi-objective image segmentation with an interactive evolutionary computation approach

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning

Quantified Score

Hi-index	0.10

Visualization

Abstract

The objective of this paper is to evaluate a new combined approach intended for reliable color image segmentation, in particular images presenting color structures with strong but continuous color or luminosity changes, such as commonly found in outdoors scenes. The approach combines an enhanced version of the Gradient Network 2, with common region-growing approaches used as pre-segmentation steps. The GNM2 is an post-segmentation procedure based on graph analysis of global color and luminosity gradients in conjunction with a segmentation algorithm to produce a reliable segmentation result. The approach was automatically evaluated using a close/open world approach. Two different region-growing segmentation methods, CSC and Mumford and Shah with and without the GNM post-processing were compared against ground truth images using segmentation evaluation indices Rand and Bipartite Graph Matching. These results were also confronted with other well established segmentation methods (RHSEG, Watershed, EDISON, JSEG and Blobworld).