Image segmentation by correlation adaptive weighted regression
| Autor: | Weiwei Wang, Cui-ling Wu |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
business.industry
Segmentation-based object categorization Cognitive Neuroscience Feature vector Scale-space segmentation 020206 networking & telecommunications Pattern recognition Image processing 02 engineering and technology Image segmentation Computer Science Applications Minimum spanning tree-based segmentation Image texture Artificial Intelligence Region growing Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Mathematics |
| Zdroj: | Neurocomputing. 267:426-435 |
| ISSN: | 0925-2312 |
| Popis: | Image segmentation aims to partition an image into several disjoint regions with each region corresponding to a visual meaningful object. It is a fundamental problem in image processing and computer vision. Recently, subspace clustering methods shows great potential in image segmentation. In this work we formulate image segmentation as subspace clustering of image feature vectors. To extend the capture ability of image varieties, we use a union of three kinds of feature including CH, LBP, and HOG. We propose an explicit data-correlation-adaptive penalty on the representation coefficients by a combination of correlation weighted l 1 -norm and l 2 -norm, and formulate the subspace representation as a Correlation Adaptive Weighted Regression (CAWR) problem. It can be regarded as a method which interpolates SSC and LSR adaptively depending on the correlation among data samples. It has subspace selection ability for uncorrelated data as well as grouping ability for highly correlated data. Experimental results of image segmentation show that the proposed model is better than the-state-of-art methods. |
| Databáze: | OpenAIRE |
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