Neural Networks for Fashion Image Classification and Visual Search
This addresses inefficiencies for ecommerce sellers and customers, but appears incremental as it explores existing methods without claiming new breakthroughs.
The paper tackles the problems of manual tagging for product images and keyword-based search limitations in ecommerce by exploring machine learning algorithms for fashion image classification and visual search, but does not report specific results or numbers.
We discuss two potentially challenging problems faced by the ecommerce industry. One relates to the problem faced by sellers while uploading pictures of products on the platform for sale and the consequent manual tagging involved. It gives rise to misclassifications leading to its absence from search results. The other problem concerns with the potential bottleneck in placing orders when a customer may not know the right keywords but has a visual impression of an image. An image based search algorithm can unleash the true potential of ecommerce by enabling customers to click a picture of an object and search for similar products without the need for typing. In this paper, we explore machine learning algorithms which can help us solve both these problems.