Video Classification using Slow Feature Analysis and Neural Network

International Journal of Electrical and Electronics Engineering
© 2017 by SSRG - IJEEE Journal
Volume 4 Issue 7
Year of Publication : 2017
Authors : Bilkis .A. Inamdar, Prof. Ujwal. Harode
How to Cite?

Bilkis .A. Inamdar, Prof. Ujwal. Harode, "Video Classification using Slow Feature Analysis and Neural Network," SSRG International Journal of Electrical and Electronics Engineering, vol. 4,  no. 7, pp. 1-5, 2017. Crossref,


Videos concepts based feature classification is a process, where the classification of videos will be classified on the basis of video classes. For classification initially frames will be extracted from the dataset then these frames will be processes for feature extraction. In our context we made an approach towards the slowness principle which has a fundamental ability to perform as close to the function of human brain to visualize the change in the surrounding and has the tendency to create slow motion output from the fast varying input signal due to the slowness it provides with a stable output as well. Using the frames from the input videos from the datasets features are extracted. Initially V1-like features is obtained as a result these features are then processed to slow feature analysis method which determines different category of video sections composed of natural scenes from the dataset input. Hence the system will be trained and motion components are computed thus using the knowledge based classification algorithm this videos will be classified according to their classes using a classifier i.e. feed forward back propagation neural network.


Unsupervised learning, motion analysis, neural network.


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