scholarly journals Classification of Polarimetric SAR Images Based on Combining Support Vector Machine Classifier and Markov Random Fields

2016 ◽  
Vol 3 (4) ◽  
pp. 1-18
Author(s):  
Ali Masjedi ◽  
Yasser Maghsoudi ◽  
Mohamad Javad Valadan Zoej ◽  
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◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Random Fields ◽  
Markov Random Fields ◽  
Support Vector Machine Classifier ◽  
Support Vector ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Markov Random

MULTI-CLUSTER SUPPORT VECTOR MACHINE CLASSIFIER FOR THE CLASSIFICATION OF SUSPICIOUS AREAS IN DIGITAL MAMMOGRAMS

2011 ◽  
Vol 10 (04) ◽  
pp. 481-494 ◽  
Author(s):  
PETER MC LEOD ◽  
BRIJESH VERMA
Keyword(s):  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Screening Mammography ◽  
Support Vector ◽  
Svm Classifier ◽  
Anova Analysis ◽  
Novel Technique ◽  
Digital Database ◽  
Benchmark Database

This paper presents a novel technique for the classification of suspicious areas in digital mammograms. The proposed technique is based on clustering of input data into numerous clusters and amalgamating them with a Support Vector Machine (SVM) classifier. The technique is called multi-cluster support vector machine (MCSVM) and is designed to provide a fast converging technique with good generalization abilities leading to an improved classification as a benign or malignant class. The proposed MCSVM technique has been evaluated on data from the Digital Database of Screening Mammography (DDSM) benchmark database. The experimental results showed that the proposed MCSVM classifier achieves better results than standard SVM. A paired t-test and Anova analysis showed that the results are statistically significant.

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