Feature subspaces selection via one-class SVM: Application to textured image segmentation
| Autor: | André Smolarz, Xiyan He, Pierre Beauseroy |
|---|---|
| Přispěvatelé: | Laboratoire Modélisation et Sûreté des Systèmes (LM2S), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Contextual image classification
business.industry Computer science Feature extraction Pattern recognition Feature selection 02 engineering and technology Image segmentation Linear subspace Support vector machine ComputingMethodologies_PATTERNRECOGNITION Image texture Feature (computer vision) 020204 information systems [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | 2010 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA) 2010 2nd International Conference on Image Processing Theory, Tools and Applications (IPTA), Jul 2010, Paris, France. pp.21-25, ⟨10.1109/IPTA.2010.5586807⟩ IPTA |
| DOI: | 10.1109/IPTA.2010.5586807⟩ |
| Popis: | International audience; This paper presents a feature subspaces selection method which uses an ensemble of one-class SVMs. The objective is to improve or preserve the performance of a decision system in the presence of noise, loss of information or feature non-stationarity. The proposed method consists in first generating an ensemble of feature subspaces from the initial full-dimensional space, and then making the decision by using only the subspaces which are supposed to be immune to the non-stationary disturbance. One particularity of this method is that we use the one-class SVM ensemble to carry out the feature selection and the classification tasks at the same time. Textured image segmentation constitutes an appropriate application for the evaluation of the proposed approach. The experimental results demonstrate the effectiveness of the decision system that we have developed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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