Self-organization in vision: Stochastic clustering for image segmentation, perceptual grouping, and image database organization

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 23, Issue 10, 2001, Pages 1053-1074

  Gdalyahu, Yoram ^b   Weinshall, Daphna ^a,c   Werman, Michael ^a,c  

^a IEEE   (Israel)

^b MobilEye Vision Technologies   (Israel)

^c HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

Clustering; Image retrieval; Perceptual grouping; Segmentation

Indexed keywords

PERCEPTUAL GROUPINGS;

ALGORITHMS; DATA STRUCTURES; DATABASE SYSTEMS; GRAPH THEORY; IMAGE RETRIEVAL; IMAGE SEGMENTATION; OBJECT RECOGNITION; SELF ORGANIZING MAPS;

COMPUTER VISION;

EID: 0035481858     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.954598     Document Type: Article

References (44)

1. Data clustering using a model granular magnet
2. Fast approximate energy minimization via graph cuts
3. Image segmentation from consensus information
4. A multibody factorization method for motion analysis
5. Modelling similarity and identification when there are momentary fluctuations in psychological amplitudes
6. Image segmentation using local variation
7. Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes
8. A randomized algorithm for pairwise clustering
9. Perceptual grouping and segmentation by stochastic clustering
10. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images
11. An on-line agglomerative clustering method for nonstationary data
12. Inferring global perceptual contours from local features
13. Figure-ground discrimination: A combinatorial optimization approach
14. Unsupervized texture segmentation in a deterministic annealing framework
15. Pairwise data clustering by deterministic annealing
16. Segmentation by grouping junctions
17. A new approach to the minimum cut problem
18. Authoritative sources in a hyperlink environment
19. A general theory of classification sorting strategies: II. Clustering systems
20. Boundary detection by minimizing functionals
21. A review on image segmentation techniques
22. A factorization approach to grouping
23. Family resemblance: Studies in the internal structure of categories
24. Constrained clustering as an optimization method
25. Quantitative measures of change based on feature organization: Eigenvalues and eigenvectors
26. Feature grouping by relocalization of eigenvectors of the proximity matrix
27. Fast multiscale image segmentation
28. Symmetry based indexing of image databases
29. Structural saliency: The detection of globally salient structures using a locally connected network
30. Toward a universal law of generalization for psychological science
31. Normalized cuts and image segmentation
32. Analytic solution of stochastic completion fields
33. Data clustering by markovian relaxation and the information bottleneck method
34. Features of similarity
35. Segmentation using eigenvectors: A unifying view
36. Stochastic completion fields: A natural model of illusory contour shape and salience
37. An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation