Survey on SVM and their application in image classification

TLDR: The different computational model of SVM and key process for the SVM system development are reviewed and a survey on their applications for image classification is provided.

Abstract: Life of any living being is impossible if it does not have the ability to differentiate between various things, objects, smell, taste, colors, etc. Human being is a good ability to classify the object easily such as different human face, images. This is time of the machine so we want that machine can do all the work like as a human, this is part of machine learning. Here this paper discusses the some important technique for the image classification. What are the techniques through which a machine can learn for the image classification task as well as perform the classification task with efficiently. The most known technique to learn a machine is SVM. Support Vector machine (SVM) has evolved as an efficient paradigm for classification. SVM has a strongest mathematical model for classification and regression. This powerful mathematical foundation gives a new direction for further research in the vast field of classification and regression. Over the past few decades, various improvements to SVM has appeared, such as twin SVM, Lagrangian SVM, Quantum Support vector machine, least square support vector machine, etc., which will be further discussed in the paper, led to the creation of a new approach for better classification accuracy. For improving the accuracy as well as performance of SVM, we must aware of how a kernel function should be selected and what are the different approaches for parameter selection. This paper reviews the different computational model of SVM and key process for the SVM system development. Furthermore provides survey on their applications for image classification.