Investigation of measures for grouping by graph partitioning

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 1, Issue , 2001, Pages

  Soundararajan, Padmanabhan ^a   Sarkar, Sudeep ^a  

^a UNIVERSITY OF SOUTH FLORIDA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

OBJECT RECOGNITION; PROBABILITY; STATISTICAL METHODS; THEOREM PROVING;

GRAPH PARTITIONING;

IMAGE SEGMENTATION;

EID: 0035686883     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. A generic grouping algorithm and its quantitative analysis
2. A framework for performance characterization of intermediate-level grouping modules
3. Graph theoretic techniques for cluster analysis algorithms
4. Supervised learning of large perceptual organization: Graph spectral partitioning and learning automata
5. Normalized cuts and image segmentation
6. A simple main cut algorithm
7. Segmentation using eigenvectors: A unifying view
8. A comparison of measures for detecting natural shapes in cluttered backgrounds
9. An optimal graph theoretic approach to data clustering: Theory and application to image segmentation