Image Analysis using Non Negative Matrix Factorization
PESN Krishna Prasad1, K. Pavan Kumar2, Y. Suresh3
1Dr. PESN Krishna Prasad*,Professor, Dept. of CSE,, Sri Venkateswara College of Engineering, Tirupati, Andhra Pradesh, India.
2Dr. K. Pavan Kumar, Asst. Prof, Dept. of IT, PVP Siddhartha Institute of technology, Kanuru, Vijayawada, A.P, India.
3Dr. Y. Suresh, Asst. Prof, Dept. of IT, PVP Siddhartha Institute of technology, Kanuru, Vijayawada, A.P, India.

Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 4156-4158 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6901018520/2020©BEIESP | DOI: 10.35940/ijrte.E6901.018520

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Abstract: Image analysis extracts the meaningful information from the images. This information is very much helpful to recognition and authentication. There are number of techniques available for image analysis. Image analysis can be used in analysis of the scene, image understanding and computer vision. Image analysis can be used in Medical image processing, Geology, optical character recognition and forensics. There are mainly four steps in the image analysis 1.image pre processing 2. Segmentation 3.feature extraction and 4. Classification and interpretation. Feature extraction is the main part for any image analysis. In this paper Multi modal biometric authentication system can be defined for security. In this process Non Negative Matrix Factorization (NMF) technique is used for feature extraction and for fusion Principle component analysis is used. After getting the features these can be encoding using Kronecker product. At the end Euclidean distance measure is used for authentication.
Keywords: Non Negative Matrix Factorization, Principle Component Analysis and Kronecker Product.
Scope of the Article: Seismic Evaluation of Building Nonstructural Components.