USING OBJECT-FOCUSED IMAGES AS AN IMAGE AUGMENTATION TECHNIQUE TO IMPROVE THE ACCURACY OF IMAGE-CLASSIFICATION MODELS WHEN VERY LIMITED DATA SETS ARE AVAILABLE

Ahmad Melhem Hammoud, Ahmad Rabih Ghandour

Keywords: Machine Learning, Computer Vision, Data Augmentation, Background Removal

Abstract: Today, many of the machine learning models are extremely data hungry. On the other hand, the accuracy of the algorithms used is very often affected by the amount of the training data available, which is, unfortunately, rarely abundant. Fortunately, image augmentation is one of the very powerful techniques that can be used by computer-vision engineers to expand their existing image data sets. This paper presents an innovative way for creating a variation of existing images and introduces the idea of using an Object-Focused Image (OFI). This is when an image includes only the labeled object and everything else is made transparent. The objective of OFI method is to expand the existing image data set and hence improve the accuracy of the model used to classify images. This paper also elaborates on the OFI approach and compares the accuracy of five different models with the same network design and settings but with different content of the training data set. The experiments presented in this paper show that using OFIs along with the original images can lead to an increase in the validation accuracy of the used model. In fact, when the OFI technique is used, the number of the images supplied nearly doubles.

One-sentence Summary: Before training an image classifying model, removing backgrounds from all training images and keeping just the labeled object will generate a new set of images, which will augment the data and increase the accuracy of the trained model

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