Image Quantification Learning Technique through Content based Image Retrieval

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Dr. R. Usha Rani


This paper proposes a Radial basis functionality incorporation in learning the quantification of images using Content based Image Retrieval (CBIR). The approach is trying to find out the effectiveness of Multi-Layer Perceptron (MLP) namely Radial Basis Function (RBF) through Content Based Image Retrieval. Extract the features of an image, the numeric values of each pixel is framed in to a definite input data set of image to that the neural networks MLP gives the accuracy of the prediction of that particular Image data set. This paper put forward us with new idea of neural networks structure efficiency in the accuracy of output data set which got increased by the adjustment of the weighted neurons through a Perceptron called Radial Basis Function promoting by applying k means clustering to form clusters which are parameterized with Gaussian function application. Finally compare the actual output with observed output promoting the weighted neurons adjustment for getting the actual accurate output. A new dimension, in work enhancement of neural networks technology with that of image processing.

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How to Cite
, D. R. U. R. (2018). Image Quantification Learning Technique through Content based Image Retrieval. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 4(1), 203–206. Retrieved from