Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Computing perceptual organization in computer vision
Computing perceptual organization in computer vision
Recent directions in netlist partitioning: a survey
Integration, the VLSI Journal
Spectral partitioning with multiple eigenvectors
Discrete Applied Mathematics - Special volume on VLSI
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Factorization Approach to Grouping
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Quantitative Measures of Change based on Feature Organization: Eigenvalues and Eigenvectors
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
An in-depth study of graph partitioning measures for perceptual organization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation with ratio cut
IEEE Transactions on Pattern Analysis and Machine Intelligence
The HCM for perceptual image segmentation
Neurocomputing
Shape reasoning on mis-segmented and mis-labeled objects using approximated Fisher criterion
Computers and Graphics
Automatic 3D object segmentation in multiple views using volumetric graph-cuts
Image and Vision Computing
Hypergraph Cuts & Unsupervised Representation for Image Segmentation
Fundamenta Informaticae
A desiderata for refactoring-based software modularity improvement
Proceedings of the 3rd India software engineering conference
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Weighted adaptive neighborhood hypergraph partitioning for image segmentation
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
A segmentation algorithm for noisy images
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
| Hi-index | 0.00 |
The goal of this communication is to suggest an alternative implementation of the k-way Ncut approach for image segmentation. We believe that our implementation alleviates a problem associated with the Ncut algorithm for some types of images: its tendency to partition regions that are nearly uniform with respect to the segmentation parameter. Previous implementations have used the k-means algorithm to cluster the data in the eigenspace of the affinity matrix. In the k-means based implementations, the number of clusters is estimated by minimizing a function that represents the quality of the results produced by each possible value of k. Our proposed approach uses the clustering algorithm of Koontz and Fukunaga in which k is automatically selected as clusters are formed (in a single iteration). We show comparison results obtained with the two different approaches to non-parametric clustering. The Ncut generated oversegmentations are further suppressed by a grouping stage - also Ncut based -in our implementation. The affinity matrix for the grouping stage uses similarity based on the mean values of the segments.