Image segmentation using local spectral histograms and linear regression
- 1 April 2012
- journal article
- research article
- Published by Elsevier BV in Pattern Recognition Letters
- Vol. 33 (5), 615-622
- https://doi.org/10.1016/j.patrec.2011.12.003
Abstract
We present a novel method for segmenting images with texture and nontexture regions. Local spectral histograms are feature vectors consisting of histograms of chosen filter responses, which capture both texture and nontexture information. Based on the observation that the local spectral histogram of a pixel location can be approximated through a linear combination of the representative features weighted by the area coverage of each feature, we formulate the segmentation problem as a multivariate linear regression, where the solution is obtained by least squares estimation. Moreover, we propose an algorithm to automatically identify representative features corresponding to different homogeneous regions, and show that the number of representative features can be determined by examining the effective rank of a feature matrix. We present segmentation results on different types of images, and our comparison with other methods shows that the proposed method gives more accurate results.Keywords
This publication has 29 references indexed in Scilit:
- Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov FieldsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
- Efficient Graph-Based Image SegmentationInternational Journal of Computer Vision, 2004
- Unsupervised image segmentation using a simple MRF model with a new implementation schemePattern Recognition, 2004
- Mean shift: a robust approach toward feature space analysisIeee Transactions On Pattern Analysis and Machine Intelligence, 2002
- Texture segmentation using Gaussian-Markov random fields and neural oscillator networksIEEE Transactions on Neural Networks, 2001
- Active contours without edgesIEEE Transactions on Image Processing, 2001
- Designing Gabor filters for optimal texture separabilityPattern Recognition, 2000
- Geodesic Active ContoursInternational Journal of Computer Vision, 1997
- Optimal Gabor filters for texture segmentationIEEE Transactions on Image Processing, 1995
- Markov Random Field Texture ModelsIeee Transactions On Pattern Analysis and Machine Intelligence, 1983