Automatic tagging and retrieval of e-commerce products based on visual features

Abstract

This paper proposes an automatic tag assignment approach to various e-commerce products where tag allotment is done solely based on the visual features in the image. It then builds a tag based product retrieval system upon these allotted tags. The explosive growth of e-commerce products being sold online has made manual annotation infeasible. Without such tags it's impossible for customers to be able to find these products. Hence a scalable approach catering to such large number of product images and allocating meaningful tags is essential and could be used to make an efficient tag based product retrieval system. In this paper we propose one such approach based on feature extraction using Deep Convolutional Neural Networks to learn descriptive semantic features from product images. Then we use inverse distance weighted K-nearest neighbours classifiers along with several other multi-label classification approaches to assign appropriate tags to our images. We demonstrate the functioning of our algorithm for the Amazon product dataset for various categories of products like clothing and apparel, electronics, sports equipment etc.