Texture descriptors based on co-occurrence matrices
Computer Vision, Graphics, and Image Processing
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
Image Segmentation by Data-Driven Markov Chain Monte Carlo
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
Windsurf: Region-Based Image Retrieval Using Wavelets
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
Proceedings of the 2003 ACM symposium on Applied computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Efficient Image Segmentation by Mean Shift Clustering and MDL-Guided Region Merging
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Bayesian Image Segmentation Using Wavelet-Based Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Image Segmentation with Fast Wavelet-Based Color Segmenting and Directional Region Growing
IEICE - Transactions on Information and Systems
Content-based image retrieval using wavelets
CEA'08 Proceedings of the 2nd WSEAS International Conference on Computer Engineering and Applications
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
Adaptive scale fixing for multiscale texture segmentation
IEEE Transactions on Image Processing
A multiple instance learning based framework for semantic image segmentation
Multimedia Tools and Applications
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Because of ubiquitous irregularities among texture patterns in real images, texture representation has long been a challenge for image analysis. Approaches such as wavelet transforms that use fixed-sized windows to extract local features are popular for texture identification and classification. However, due to the unawareness of texture scales and boundary locations, those block-based approaches have limited success for image segmentation. In this paper, we present a novel algorithm that tends to generate statistical descriptors that are adaptive to the variation of texture patterns based on a simple rule of pruning and concatenating the approximately repetitive patterns. In the context of image segmentation, the color information that is used by the popular mean shift segmentation algorithm is usually not sufficient for good segmentation performance. We alleviate this problem by first preprocessing the image with the proposed method to generate a "texture map" which then becomes the input image representation for the mean shift segmentation algorithm. The experimental results demonstrate the robustness and effectiveness of the proposed texture representation.