Reverse image search
At first glance the implementation of this type of searchers may seem unnecessary, but due to the continuous documentary inflation of the Internet, every day it becomes more necessary indexing information.
Mobile Visual Search (MVS) bridges the gap between online and offline media, enabling you to link your customers to digital content.
Mobile phones have evolved into powerful image and video processing devices equipped with high-resolution cameras, color displays, and hardware-accelerated graphics.
All this enables a new class of applications that use the camera phone to initiate search queries about objects in visual proximity to the user (Figure 1).
Such applications can be used, e.g., for identifying products, comparison shopping, finding information about movies, compact disks (CDs), real estate, print media, or artworks.
Once this whole procedure is done, a central computer will analyze the data and create a page of the results sorted with the efficiency of each team, to eventually be sent to the mobile phone.
For unauthorized use, Pixsy offers a compensation recovery service[13][14] for commercial use of the image owners work.
[15] eBay ShopBot uses reverse image search to find products by a user uploaded photo.
[16] SK Planet uses reverse image search to find related fashion items on its e-commerce website.
It developed the vision encoder network based on the TensorFlow inception-v3, with speed of convergence and generalization for production usage.
A recurrent neural network is used for multi-class classification, and fashion-product region-of interest detection is based on Faster R-CNN.
Pailitao (Chinese: 拍立淘, literally means shopping through a camera) allows users to search for items on Alibaba's E-commercial platform by taking a photo of the query object.
The system is operated by Amazon EC2, and only requires a cluster of 5 GPU instances to handle daily image uploads onto Pinterest.
By using reverse image search, Pinterest is able to extract visual features from fashion objects (e.g. shoes, dress, glasses, bag, watch, pants, shorts, bikini, earrings) and offer product recommendations that look similar.
[21][22] JD.com disclosed the design and implementation of its real time visual search system at the Middleware '18 conference.
The peer reviewed paper focuses on the algorithms used by JD's distributed hierarchical image feature extraction, indexing and retrieval system, which has 300 million daily active users.
[24] Amazon.com disclosed the architecture of a visual search engine for fashion and home products named Amazon Shop the Look in a paper published at the KDD'22 conference.
The paper describes the lessons learned by Amazon when deployed in production environment, including image synthesis-based data augmentation for retrieval performance optimization and accuracy improvement.
Arista-DS only performs duplicate search algorithms such as principal component analysis on global image features to lower computational and memory costs.
[29] In 2019, a book published by O'Reilly documents how a simple reverse image search system can be built in a few hours.
