Feature Selection Based on Minimum Overlap Probability (MOP) in Identifying Beef and Pork

Feature selection is one of the most important techniques in image processing for classifying. In classifying beef and pork based on texture feature, feature overlaps are difficult issues. This paper proposed feature selection method by Minimum Overlap Probability (MOP) to get the best feature. The...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2016-01, Vol.7 (3)
Main Authors: Anwar, Khoerul, Harjoko, Agus, Suharto, Suharto
Format: Article
Language:English
Subjects:
Artificial neural networks
Back propagation
Back propagation networks
Beef
Digital imaging
Feature selection
Image classification
Image processing
Neural networks
Pork
Training
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Summary:Feature selection is one of the most important techniques in image processing for classifying. In classifying beef and pork based on texture feature, feature overlaps are difficult issues. This paper proposed feature selection method by Minimum Overlap Probability (MOP) to get the best feature. The method was tested on two datasets of features of digital images of beef and pork which had similar textures and overlapping features. The selected features were used for data training and testing by Backpropagation Neural Network (BPNN). Data training process used single features and several selected feature combinations. The test result showed that BPNN managed to detect beef or pork images with 97.75% performance. From performance a conclusion was drawn that MOP method could be used to select the best features in feature selection for classifying/identifying two digital image objects with similar textures.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2016.070345