Bin Ratio-Based Histogram Distances and their Application to Image Classification

International Journal of Electrical and Electronics Engineering

© 2017 by SSRG - IJEEE Journal

Volume 4 Issue 8

Year of Publication : 2017

Authors : Sonam Agarwal, Prof. Ujwal. Harode

10.14445/23488379/IJEEE-V4I8P105

How to Cite?

Sonam Agarwal, Prof. Ujwal. Harode, "Bin Ratio-Based Histogram Distances and their Application to Image Classification," SSRG International Journal of Electrical and Electronics Engineering, vol. 4,  no. 8, pp. 29-32, 2017. Crossref, https://doi.org/10.14445/23488379/IJEEE-V4I8P105

Abstract:

Image representation in form of Histogram plays an important role in image classification, action and pattern recognition. Differences i.e. distances can be well identified and studied using a histogram plot. It is the representation in the graphical form of the plot of tonal distribution of digital image. It is the plot of pixels against tonal value. One can judge the tonal distribution in an image just by looking the Histogram plot. When it comes to the local Patches i.e. the distribution of local areas of image, Quantization of patches by sub-histogram is good approach. Histogram followed by sub-histogram and different distance classifier along with SVM gives good accuracy level. There are many modern technologies coming up like expert system classifier, ANN and DTC that maximize the accuracy level. The report will summarize the advance classification approaches that are used to improve accuracy levels.

Keywords:

SVM, DTC, Artificial Neural Network, Expert System Classifier.

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