DOIONLINE 
DOIONLINE Publish In |
International Journal of Soft Computing And Artificial Intelligence (IJSCAI)-IJSCAI |
 Journal Home Volume Issue |
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Issue |
Volume-10,Issue-1 ( May, 2022 ) | |||||||||
Paper Title |
Comparing CNN and CNN-SVM for Food Image Recognition | |||||||||
Author Name |
Aishwarya Birla, Rasesh Sabu, N.Snehalatha | |||||||||
Affilition |
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Pages |
1-4 | |||||||||
Abstract |
Abstract - Convolution neural network (CNN) currently dominates the marketplace and is more popular in image classification modeling around the world. It works on the basic principle of assigning importance to certain sections of images and then finding the similarity or rather the difference between them. Support Vector Machines (SVM) on the other hand construct a hyperplane in a multi-dimensional space, iterating so as to avoid errors and divide the image into various parts, segregating and then differentiating them. In this paper our aim is to compare Convolutional Neural Networks (CNN) and Convolutional Neural Networks – Support Vector Mechanism (CNN-SVM) for food image recognition and come up with a better method for image classification. Keywords - Convolution Neural Network, Support Vector Machines, SoftMax, Food Image, CNN-SVM | |||||||||
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