Color image segmentation using tensor voting based color clustering

Authors:
Toan Dinh Nguyen;Gueesang Lee
Affiliations:
Department of Electronics and Computer Engineering, Chonnam National University, Gwangju, Republic of Korea;Department of Electronics and Computer Engineering, Chonnam National University, Gwangju, Republic of Korea
Venue:
Pattern Recognition Letters
Year:
2012

Citing 15
Cited 0

On the color image segmentation algorithm based on the thresholding and the fuzzy C-means techniques

Pattern Recognition
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Inference of Surfaces, 3D Curves, and Junctions from Sparse, Noisy, 3D Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate nearest neighbor queries in fixed dimensions

SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
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
Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Framework for Segmentation and Grouping

Computational Framework for Segmentation and Grouping
Performance Evaluation of Some Clustering Algorithms and Validity Indices

IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Region Merging

IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene Text Extraction Using Focus of Mobile Camera

ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Tensor Voting Based Color Clustering

ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A Cluster Separation Measure

IEEE Transactions on Pattern Analysis and Machine Intelligence
Some new indexes of cluster validity

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Soft Color Segmentation and Its Applications

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.10

Visualization

Abstract

A novel color image segmentation method using tensor voting based color clustering is proposed. By using tensor voting, the number of dominant colors in a color image can be estimated efficiently. Furthermore, the centroids and structures of the color clusters in the color feature space can be extracted. In this method, the color feature vectors are first encoded by second order, symmetric, non-negative definite tensors. These tensors then communicate with each other by a voting process. The resulting tensors are used to determine the number of clusters, locations of the centroids, and structures of the clusters used for performing color clustering. Our method is based on tensor voting, a non-iterative method, and requires only the voting range as its input parameter. The experimental results show that the proposed method can estimate the dominant colors and generate good segmented images in which those regions having the same color are not split up into small parts and the objects are separated well. Therefore, the proposed method is suitable for many applications, such as dominant colors estimation and multi-color text image segmentation.