An efficient color image segmentation algorithm using hybrid approaches

Authors:
R. Srinivasa Perumal;P. V. S. S. R. Chandra Mouli
Affiliations:
VIT University, Vellore;VIT University, Vellore
Venue:
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Year:
2010

Citing 12
Cited 0

Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally Optimal Regions and Boundaries as Minimum Ratio Weight Cycles

IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Introduction to Algorithms

Introduction to Algorithms
Introduction to the Special Section on Graph Algorithms in Computer Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
An empirical approach to grouping and segmentation

An empirical approach to grouping and segmentation
Efficient Graph-Based Image Segmentation

International Journal of Computer Vision
Digital Image Processing (3rd Edition)

Digital Image Processing (3rd Edition)
Supervised Learning of Edges and Object Boundaries

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters

IEEE Transactions on Computers
Image Segmentation Based on Minimal Spanning Tree and Cycles

ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 03
Multi-scale Improves Boundary Detection in Natural Images

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

Color image segmentation is still a challenging problem. Literature reveals many supervised algorithms wherein the primary input is the number of segments to which the image is to be segmented. Currently researchers are focusing on unsupervised segmentation algorithms. The main advantage of the proposed method is that no a priori information is required to segment the given color image and hence considered as an unsupervised approach. The proposed method is found to be reliable and works satisfactorily on different kinds of color images. Subjective comparison and objective evaluation shows the efficacy of the proposed method over other existing methods.