Abstract: This paper presents an end-to-end large―scale visual search system. Several challenges were met during its development: dealing with heptamerous image data, indexing large―scale images for massive data updating, training deep learning models for effective feature representation without a lot of manual work, improving the latency and providing accurate results. We assessed our system on a publicly available dataset consisting of flowers commonly available in the UK. This paper describes our implementation in great details as well as our lessons learnt during the building of such a large-scale system. We used locality sensitive hashing to perform the nearest neighbor search and given the data size, we relied on the random projection technique. In order to represent the image in a smaller dimension we used Bit-ResNet as the underlying model given its fine-tuning setup. Extensive experiments show that our system provides satisfactory results.

 Keywords: Additional Key Words and Phrases: datasets, neural networks, visual search, locality sensitive hashing