Learning a classification model for segmentation
Top Cited Papers
- 1 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 10-17 vol.1
- https://doi.org/10.1109/iccv.2003.1238308
Abstract
We propose a two-class classification model for grouping. Human segmented natural images are used as positive examples. Negative examples of grouping are constructed by randomly matching human segmentations and images. In a preprocessing stage an image is over-segmented into super-pixels. We define a variety of features derived from the classical Gestalt cues, including contour, texture, brightness and good continuation. Information-theoretic analysis is applied to evaluate the power of these grouping cues. We train a linear classifier to combine these features. To demonstrate the power of the classification model, a simple algorithm is used to randomly search for good segmentations. Results are shown on a wide range of images.Keywords
This publication has 20 references indexed in Scilit:
- Non-parametric similarity measures for unsupervised texture segmentation and image retrievalPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statisticsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A Probabilistic Multi-scale Model for Contour Completion Based on Image StatisticsPublished by Springer Nature ,2002
- The Elements of Statistical LearningPublished by Springer Nature ,2001
- A factorization approach to groupingPublished by Springer Nature ,1998
- Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996
- Optimal approximations by piecewise smooth functions and associated variational problemsCommunications on Pure and Applied Mathematics, 1989
- Radial projection: an efficient update rule for relaxation labelingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Feature detection from local energyPattern Recognition Letters, 1987
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1984