Deep Learning Machine using Hierarchical Cluster Features

Sara Salman, Jamila H. Soud

Abstract


Deep learning of multi-layer computational models allowed processing to recognize data representation at multiple levels of abstraction. These techniques have greatly improved the latest ear recognition technology. PNN is a type of radiative basis for classification problems and is based on the Bayes decision-making base, which reduces the expected error of classification. In this paper, strong features of images are used to give a good result, therefore, SIFT method using these features after adding improvements and developments. This method was one of the powerful algorithms in matching that needed to find energy pixels. This method gives stronger feature on features and gives a large number of a strong pixel, which is considered a center and neglected the remainder of it in our work.
Each pixel of which is constant for image translation, scaling, rotation, and embedded lighting changes in lighting or 3D projection. Therefore, the interpretation is developed by using a hierarchical cluster method; to assign a set of properties (find the approximation between pixels) were classified into one.

Keywords


Scale Invariant Feature Transform (SIFT), Probabilistic Neural Networks (PNN), Hierarchical Cluster.

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References


D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis., vol. 60, no. 2, pp. 91–110, Nov. 2004.

M. Aly, “Face Recognition using SIFT Features”, http://www.vision. caltech.edu /malaa/research.php

DakshinaRanjanKisku, AjitaRattani, Enrico Grosso, Massimo Tistarelli,”.Face Identification by SIFT-based Complete Graph Topology”

J. Krizaj, V. Struc, and N. Pavesic “Adaptation of SIFT feature for Robust Face Recognition”, PP 394-404, 2010.

A. Majumdar, R. K. Ward, “Discriminative SIFT Features for Face Recognition,” Department of Electrical and Computer Engineering, University of British Columbia.

Prof.Dr.Jamila Harbi S., Sara Salman,” Edge Detection of Ear ImageBased on Canny Method”

Nabha B. Nimbhorkar and Satish J. Alaspurkar, "Probabilistic Neural Network in Solving Various Pattern Classification Problems", IJCSNS International Journal of Computer Science and Network Security, VOL.14 No.3, in March 2014.

Hajmeer M. and Banshee, I., "A Probabilistic Neural Network Approach for Modeling and Classification of Bacterial Growth/no-Growth Data", Journal of Microbiological Methods, pp 217– 226, in 2002.

Way Soong Lim and M.V.C. Rao, "A New Method of Reducing Network Complexity in Probabilistic Neural Network for Target Identification", IEICE Electronics Express, Vol.1, No.17, 534-539, Faculty of Engineering & Technology, Multimedia University, in December 2004.

Revett, K., Gorunescu, F., Gorunescu, M., Ene, M., Tenreiro, S., and Henrique Dinis Santos, M.," A Machine Learning Approach to Keystroke Dynamics Based User Authentication", Int. J. Electronic Security and Digital Forensics, Vol. 1, No. 1, 2007.

Souham Meshoul and Mohamed Batouche, "Combining Fisher Discriminant Analysis and Probabilistic Neural Network for Effective On-Line Signature Recognition", 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA), IEEE, in 2010.




DOI: http://dx.doi.org/10.23851/mjs.v29i3.625

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