Comparison of Supervised Learning Image Classification Algorithms for Food and Non-Food Objects

• Published in: 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM) pp. 317 - 324
• Main Authors: Yogaswara, Reza Dea, Wibawa, Adhi Dharma
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2018
• Subjects: Artificial neural networks; Biological neural networks; Biological system modeling; convolutional neural network; deep learning; Feature extraction; image recognition; Logistics; machine learning; Neurons; object classification; Training
• DOI: 10.1109/CENIM.2018.8711387

Object recognition is a method in the computer vision to identify and recognize objects in the picture or video. When humans see photos or watch videos, they can quickly recognize some object like a car, bus, human, cat, food, and other visual artifacts. However, how do we apply it to the computer? Classification is the technique or method in object recognition that can be used on a computer to distinguish one object from another object contained in the image or video. In this paper, the author proposes about testing some popular image binary classification algorithms used along with the results of the performance matrix of each algorithm, among these are Logistic Regression with Perceptron, Multi-Layer Perceptron (MLP), Deep Multi-Layer Perceptron, and Convolutional Neural Network (ConvNet). The author uses the Food-5K dataset to distinguish two classes of objects, namely food /non-food, and then try to train and test how accurate the computer is in recognizing food and non-food objects, where it will be useful to anyone who needs to identify a food object using auto recognizing tools. This paper is expected to contribute in the field of computer vision related algorithm that is used to solve the problem in image classification, with the state of optimal hyperparameter and validation accuracy level above 90%. From the test results obtained the level of testing accuracy using ConvNet reached above 90% and loss function less than 25% while indicating that ConvNet has a significant advantage on the image classification problem compared to the generic artificial neural network.