A Comparative Experiment in Classifying Jewelry Images Using Convolutional Neural Networks

Abstract

A machine learning approach has been used in this work to categorize jewelry images into five different classes. This classification was achieved by using the convolutional neural network (CNN). The objective was to find different approaches that can be competent for the image classification and recognition. The images used in this work are drawn directly from the jewelry industries and companies. The first technique uses support vector machine along with the features that were extracted from the input images using AlexNet. The second method involves the use of Inception-v3 model for performing the same. Upon experimenting, it was derived that both the approaches performed well, however, Inception-v3 was found to be more successful by 0.9%. The Inception-v3 was then further taken to train the dataset from scratch which resulted in better consistency.