Multi class Support Vector Machines classifier for machine vision application
| Autor: | K. Annamalai Vignesh, C Ashok, R. Adithyan, J. Suriya Prakash |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Probabilistic classification
Structured support vector machine Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Linear classifier Quadratic classifier Machine learning computer.software_genre Support vector machine Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION Radial basis function kernel Margin classifier Artificial intelligence business computer |
| Zdroj: | 2012 International Conference on Machine Vision and Image Processing (MVIP). |
| DOI: | 10.1109/mvip.2012.6428794 |
| Popis: | Classification of objects has been a significant area of concern in machine vision applications. In recent years, Support Vector Machines (SVM) is gaining popularity as an efficient data classification algorithm and is being widely used in many machine vision applications due to its good data generalization performance. The present paper describes the development of multi-class SVM classifier employing one-versus-one max-wins voting method and using Radial Basis Function (RBF) and Linear kernels. The developed classifiers have been applied for color-based classification of apple fruits into three pre-defined classes and their performance is compared with conventional K-Nearest Neighbor (KNN) and Naive Bayes classifiers. The multi-class SVM classifier with RBF kernel has shown superior classification performance. |
| Databáze: | OpenAIRE |
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