A color image segmentation approach for content-based image retrieval

Authors:
Mustafa Ozden;Ediz Polat
Affiliations:
Electrical and Electronics Engineering Department, Kirikkale University, 71450 Kirikkale, Turkey;Electrical and Electronics Engineering Department, Kirikkale University, 71450 Kirikkale, Turkey
Venue:
Pattern Recognition
Year:
2007

Citing 14
Cited 13

Stochastic Relaxation on Partitions With Connected Components and Its Application to Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years

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
Mean Shift, Mode Seeking, and Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Texture Segmentation

SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Color- and Texture-Based Image Segmentation Using EM and Its Application to Content-Based Image Retrieval

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Color and texture image retrieval using chromaticity histograms and wavelet frames

IEEE Transactions on Multimedia
Automatic watershed segmentation of randomly textured color images

IEEE Transactions on Image Processing
Texture segmentation using filters with optimized energy separation

IEEE Transactions on Image Processing
Statistical texture characterization from discrete wavelet representations

IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames

IEEE Transactions on Image Processing

A robust approach to segment desired object based on salient colors

Journal on Image and Video Processing - Color in Image and Video Processing
Colors of the past: color image segmentation in historical topographic maps based on homogeneity

Geoinformatica
Unsupervised image retrieval framework based on rule base system

Expert Systems with Applications: An International Journal
Supporting image retrieval framework with rule base system

Knowledge-Based Systems
Using color chains similarities for MLB sports image retrieval

CSECS '10 Proceedings of the 9th WSEAS international conference on Circuits, systems, electronics, control & signal processing
Color texture image segmentation based on neutrosophic set and wavelet transformation

Computer Vision and Image Understanding
Image segmentation based on the integration of colour-texture descriptors-A review

Pattern Recognition
Von Kries model under Planckian illuminants

ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Spatial color image segmentation based on finite non-Gaussian mixture models

Expert Systems with Applications: An International Journal
Weak convergence of the parzen-type probabilistic neural network handling time-varying noise

ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Strong convergence of the recursive parzen-type probabilistic neural network handling nonstationary noise

ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
A stochastic gravitational approach to feature based color image segmentation

Engineering Applications of Artificial Intelligence
Efficient algorithm for automatic road sign recognition and its hardware implementation

Journal of Real-Time Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper describes a new color image segmentation method based on low-level features including color, texture and spatial information. The mean-shift algorithm with color and spatial information in color image segmentation is in general successful, however, in some cases, the color and spatial information are not sufficient for superior segmentation. The proposed method addresses this problem and employs texture descriptors as an additional feature. The method uses wavelet frames that provide translation invariant texture analysis. The method integrates additional texture feature to the color and spatial space of standard mean-shift segmentation algorithm. The new algorithm with high dimensional extended feature space provides better results than standard mean-shift segmentation algorithm as shown in experimental results.