Normalized Cuts and Image Segmentation
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
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
An empirical approach to grouping and segmentation
An empirical approach to grouping and segmentation
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
IEEE Transactions on Pattern Analysis and Machine Intelligence
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
A Lattice Approach to Image Segmentation
Journal of Mathematical Imaging and Vision
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold models for signals and images
Computer Vision and Image Understanding
On the morphological processing of hue
Image and Vision Computing
On local intrinsic dimension estimation and its applications
IEEE Transactions on Signal Processing
Region merging techniques using information theory statistical measures
IEEE Transactions on Image Processing
Morphological operators on the unit circle
IEEE Transactions on Image Processing
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Sound segmentation technology is the central in image analysis and computer vision. In this paper, a granule-based approach to color image segmentation is introduced, formulated by combining color jump connection-based granule construction with manifold learning-based feature extraction technique. Aiming at characterizing irregular objects in an image, a granulitized model is established by using techniques of jump connected segmentation and morphological reconstruction to accurately represent objects. Laplacian eigenmaps (LE) manifold learning technique is applied to extract features of granules automatically, which allows taking smooth and texture information into consideration effectively. Markov Chain Monte Carlo (MCMC) method is explored for the process of granule merging. Experiments demonstrate that the proposed approach to color image segmentation reaches high precision of granulitized representation and reliable feature characterization of objects, and yields promising segmentation results.