ViSeR: A Visual Search Engine for e-Retail

Web search engines play a significant role in many applications in our everyday lives. Among them, online shopping is one major technological advancement which has made our life easier and comfortable. Currently, most e-commerce websites support either text-based or voice-based search. The problem with text and voice-based approaches is that they need an appropriate item name or description for the search results to be accurate. Also, with the huge variety of items available online, it is not easy to find the desired object in the top results. Lately, some top e-commerce websites started supporting visual search, where the user can submit an image of an item they'd like to find. However, this domain is still in its infancy. In this work, we propose ViSeR, a visual search engine architecture using deep learning and machine learnging techniques, with a proof-of-concept implementation focusing on the fashion eretail industry. ViSeR first classifies the query image to the right category using image classification. Then all the images in that category are ranked based on their similarity and the top images are retrieved as recommendations. We present in detail the experimental results with different deep and machine learning algorithms and provide additional details with regards to deploying this model to achieve high accuracy and low latency (in terms of training and recommendation time).