A Validity Measure for Fuzzy Clustering
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
Multiresolution-based watersheds for efficient image segmentation
Pattern Recognition Letters
Neurofuzzy and neutrosophic approach to compute the rate of change in new economies
Proceedings of the first international conference on Neutrosophy, neutrosophic logic, neutrosophic set, neutrosophic probability and statistics
Edge Flow: A Framework of Boundary Detection and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Wavelet-Based Watershed Image Segmentation for VOP Generation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A color image segmentation approach for content-based image retrieval
Pattern Recognition
Unsupervised multiscale segmentation of color images
Pattern Recognition Letters
Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification
Expert Systems with Applications: An International Journal
Multi-scale Improves Boundary Detection in Natural Images
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
New neutrosophic approach to image segmentation
Pattern Recognition
Interactive image segmentation by maximal similarity based region merging
Pattern Recognition
Robust watershed segmentation using wavelets
Image and Vision Computing
Active contours with selective local or global segmentation: A new formulation and level set method
Image and Vision Computing
EdgeFlow: a technique for boundary detection and image segmentation
IEEE Transactions on Image Processing
Indirect immunofluorescence image classification using texture descriptors
Expert Systems with Applications: An International Journal
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Efficient and effective image segmentation is an important task in computer vision and pattern recognition. Since fully automatic image segmentation is usually very hard for natural images, interactive schemes with a few simple user inputs are good solutions. In this paper, we propose a fully automatic new approach for color texture image segmentation based on neutrosophic set (NS) and multiresolution wavelet transformation. It aims to segment the natural scene images, in which the color and texture of each region does not have uniform statistical characteristics. The proposed approach combines color information with the texture information on NS and wavelet domain for segmentation. At first, it transforms each color channel and the texture information of the input image into the NS domain independently. The entropy is defined and employed to evaluate the indeterminacy of the image in NS domain. Two operations, @a-mean and @b-enhancement operations are proposed to reduce the indeterminacy. Finally, the proposed method is employed to perform image segmentation using a @c-K-means clustering. The determination of the cluster number K is carried out with cluster validity analysis. Two different segmentation evaluation criterions were used to determine the segmentations quality. Experiments are conducted on a variety of images, and the results are compared with those new existing segmentation algorithm. The experimental results demonstrate that the proposed approach can segment the color images automatically and effectively.