Feature Selection Based on Minimum Overlap Probability (MOP) in Identifying Beef and Pork
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 3
Abstract
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.
Authors and Affiliations
Khoerul Anwar, Agus Harjoko, Suharto Suharto
Keywords
Related Articles
- EP ID EP138565
- DOI 10.14569/IJACSA.2016.070345
- Views 117
- Downloads 0
Fast Hybrid String Matching Algorithm based on the Quick-Skip and Tuned Boyer-Moore Algorithms
The string matching problem is considered as one of the most interesting research areas in the computer science field because it can be applied in many essential different applications such as intrusion detection, search...
Age Estimation Based on AAM and 2D-DCT Features of Facial Images
This paper proposes a novel age estimation method - Global and Local feAture based Age estiMation (GLAAM) - relying on global and local features of facial images. Global features are obtained with Active Appearance Model...
Multispectral Image Analysis using Decision Trees
Many machine learning algorithms have been used to classify pixels in Landsat imagery. The maximum likelihood classifier is the widely-accepted classifier. Non-parametric methods of classification include neural networks...
Improving Modified Grey Relational Method for Vertical Handover in Heterogeneous Networks
With the advent of next-generation wireless network technologies, vertical handover has become indispensable to keep the mobile user always best connected (ABC) in a heterogeneous environment, especially the significant...
Hybrid Feature Extraction Technique for Face Recognition
This paper presents novel technique for recognizing faces. The proposed method uses hybrid feature extraction techniques such as Chi square and entropy are combined together. Feed forward and self-organizing neura...