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
Signal Processing, Image Processing and Pattern Recognition
Signal Processing, Image Processing and Pattern Recognition
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
A Measure for Objective Evaluation of Image Segmentation Algorithms
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
Color texture segmentation based on the modal energy of deformable surfaces
IEEE Transactions on Image Processing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Least squares quantization in PCM
IEEE Transactions on Information Theory
Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Denoising by Averaging of Piecewise Constant Simulations of Image Partitions
IEEE Transactions on Image Processing
Segmentation by Fusion of Histogram-Based -Means Clusters in Different Color Spaces
IEEE Transactions on Image Processing
MDS-based segmentation model for the fusion of contour and texture cues in natural images
Computer Vision and Image Understanding
Multiple instance learning based on positive instance selection and bag structure construction
Pattern Recognition Letters
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This paper presents a new and simple segmentation method based on the K-means clustering procedure and a two-step process. The first step relies on an original de-texturing procedure which aims at converting the input natural textured color image into a color image, without texture, that will be easier to segment. Once, this de-textured (color) image is estimated, a final segmentation is achieved by a spatially-constrained K-means segmentation. These spatial constraints help the iterative K-means labeling process to succeed in finding an accurate segmentation by taking into account the inherent spatial relationships and the presence of pre-estimated homogeneous textural regions in the input image. This procedure has been successfully applied on the Berkeley image database. The experiments reported in this paper demonstrate that the proposed method is efficient (in terms of visual evaluation and quantitative performance measures) and performs competitively compared to the best existing state-of-the-art segmentation methods recently proposed in the literature.