International Journal of Advanced Networking and Applications

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Classifying content-Based Images using Self Organizing Map Neural Networks Based on Nonlinear Features


Affiliations
1 Electrical and Computer Engineering Department, Tehran Science and Research University, Tehran, Iran, Islamic Republic of
2 Department of Electrical Computer and Biomedical Engineering, Qazvin Branch Islamic Azad University, Qazvin, Iran, Islamic Republic of
3 Department of Electrical Computer and Biomedical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran, Islamic Republic of
4 Sohrevardi Private High Educational Institute, School of Computer Engineering, Qazvin, Iran, Islamic Republic of
 

Classifying similar images is one of the most interesting and essential image processing operations. Presented methods have some disadvantages like: low accuracy in analysis step and low speed in feature extraction process. In this paper, a new method for image classification is proposed in which similarity weight is revised by means of information in related and unrelated images. Based on researchers' idea, most of real world similarity measurement systems are nonlinear. Thus, traditional linear methods are not capable of recognizing nonlinear relationship and correlation in such systems. Undoubtedly, Self Organizing Map neural networks are strongest networks for data mining and nonlinear analysis of sophisticated spaces purposes. In our proposed method, we obtain images with the most similarity measure by extracting features of our target image and comparing them with the features of other images. We took advantage of NLPCA algorithm for feature extraction which is a nonlinear algorithm that has the ability to recognize the smallest variations even in noisy images. Finally, we compare the run time and efficiency of our proposed method with previous proposed methods.

Keywords

Self Organizing Maps (SOM), Nonlinear Dimensionality Reduction, Recognizing Content-Based Images, Artificial Neural Networks, Feature Vector, Machine Learning, Support Vector Machine, Clustering.
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  • Classifying content-Based Images using Self Organizing Map Neural Networks Based on Nonlinear Features

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Authors

Ebrahim Parcham
Electrical and Computer Engineering Department, Tehran Science and Research University, Tehran, Iran, Islamic Republic of
Monireh Pournazari
Department of Electrical Computer and Biomedical Engineering, Qazvin Branch Islamic Azad University, Qazvin, Iran, Islamic Republic of
Mina Hojati
Department of Electrical Computer and Biomedical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran, Islamic Republic of
Mehrdad Jalili Monir
, Iran, Islamic Republic of
Bahareh Mirzaei
Sohrevardi Private High Educational Institute, School of Computer Engineering, Qazvin, Iran, Islamic Republic of

Abstract


Classifying similar images is one of the most interesting and essential image processing operations. Presented methods have some disadvantages like: low accuracy in analysis step and low speed in feature extraction process. In this paper, a new method for image classification is proposed in which similarity weight is revised by means of information in related and unrelated images. Based on researchers' idea, most of real world similarity measurement systems are nonlinear. Thus, traditional linear methods are not capable of recognizing nonlinear relationship and correlation in such systems. Undoubtedly, Self Organizing Map neural networks are strongest networks for data mining and nonlinear analysis of sophisticated spaces purposes. In our proposed method, we obtain images with the most similarity measure by extracting features of our target image and comparing them with the features of other images. We took advantage of NLPCA algorithm for feature extraction which is a nonlinear algorithm that has the ability to recognize the smallest variations even in noisy images. Finally, we compare the run time and efficiency of our proposed method with previous proposed methods.

Keywords


Self Organizing Maps (SOM), Nonlinear Dimensionality Reduction, Recognizing Content-Based Images, Artificial Neural Networks, Feature Vector, Machine Learning, Support Vector Machine, Clustering.