Unsupervised feature learning using deep learning approaches and applying on the image matching context

Image matching is quite challenging task to identify the matching images in the data. There are multiple methods in computer vision techniques such as histogram based algorithms, color/edge based algorithms, textual based features, SIFT and Surf algorithms which will help to identify the similar images. Here in our paper we are addressing an Industrial problem to provide the better solution where US multinational courier delivery services facing challenges in delivering the products where labels/tags and barcodes of the products are missed while delivering to the customers and customer comes with the product image and with some information about the product. The job is to map the user/customer product information with the existing missed products in the database in order to deliver them. This entire process currently goes manual and it takes lot of time to address the missed products. The advances in computer science and availability of GPU machines, the problem will be addressed and solution can be automated using deep learning approaches. The paper describes the solution for matching the images accurately and comparing the solution with the existing classical computer vision algorithms.