Reverse image search
Reverse image search is a content-based image retrieval (CBIR) query technique that involves providing the CBIR system with a sample image that it will then base its search upon; in terms of information retrieval, the sample image is very useful in its ways. In particular, reverse image search is characterized by a lack of search terms. This effectively removes the need for a user to guess at keywords or terms that may or may not return a correct result. Reverse image search also allows users to discover content that is related to a specific sample image , popularity of an image, and discover manipulated versions and derivative works. Uses. Reverse image search may be used to: Algorithms. Commonly used reverse image search algorithms include: Application in popular search systems. Google Images. Google's Search by Image is a feature that uses reverse image search and allows users to search for related images just by uploading an image or image URL. Google accomplishes this by analyzing the submitted picture and constructing a mathematical model of it using advanced algorithms. It is then compared with billions of other images in Google's databases before returning matching and similar results . When available, Google also uses metadata about the image such as description. TinEye. TinEye is a search engine specialized for reverse image search. Upon submitting an image, TinEye creates a "unique and compact digital signature or fingerprint" of said image and matches it with other indexed images. This procedure is able to match even very edited versions of the submitted image, but will not usually return similar images in the results. Pixsy. Pixsy reverse image search technology detects image matches on the public internet for images uploaded to the Pixsy platform. New matches are automatically detected and alerts sent to  the user. For unauthorized use, Pixsy offers a compensation recovery service for commercial use of the image owners work. Pixsy partners with over 25 law firms and attorneys around the world to bring resolution for copyright infringement. Pixsy is the strategic image monitoring service for the Flickr platform and users. eBay. eBay ShopBot uses reverse image search to find products by a user uploaded photo. eBay uses a ResNet-50 network for category recognition, image hashes are stored in Google Bigtable; Apache Spark jobs are operated by Google Cloud Dataproc for image hash extraction; and the image ranking service is deployed  by Kubernetes. SK Planet. 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". SK Planet's reverse image search system is built in less than 100 man-months. Alibaba. Alibaba released the "Pailitao" application during 2014. Pailitao (, 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 Pailitao application uses a deep CNN model with branches for joint detection and feature learning to discover the detection mask and exact discriminative feature without background disturbance. GoogLeNet V1 is employed as the base model for category prediction and feature learning. Pinterest. Pinterest acquired startup company "VisualGraph" in 2014 and introduced visual search on its platform. In 2015, Pinterest published a paper at the ACM Conference on Knowledge Discovery and Data Mining conference and disclosed the architecture of the system. The pipeline uses Apache Hadoop , the open-source "Caffe" convolutional neural network framework, "Cascading" for batch processing, "PinLater" for messaging, and Apache HBase for storage. Image characteristics, including local features, deep features, salient color signatures and salient pixels are extracted from user uploads. 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. LykDat. LykDat uses reverse image search to  find fashion products across various online stores on the web. LykDat also provides a Twitter bot that helps users carry out reverse image searches of photos they find within Twitter. JD.com. 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. The system was able to sustain 80 million updates to its database per hour when it was deployed in production in 2018. Bing . Microsoft Bing published the architecture of their reverse image searching of system at the KDD'18 conference. The paper states that a variety of features from a query image submitted by a user are used to describe its content, including using deep neural network encoders, category recognition features, face recognition features, color features and duplicate detection features. Reverse Image Search. Reverse Image Search Reverse Image Search is a CBI (contact-based image) technology, which reverses images using CBIR system and searches similar photos on google. face recognition features, duplicate detection features. Research systems. Microsoft Research Asia's Beijing Lab published a  paper in the Proceedings of the IEEE on the Arista-SS (Similar Search) and the Arista-DS (Duplicate Search) systems. Arista-DS only performs duplicate search algorithms such as principal component analysis on global image features to lower computational and memory costs. Arista-DS is able to perform duplicate search on 2 billion images with 10 servers but with the trade-off of not detecting near duplicates. Open-source implementations. In 2007, the Puzzle library is released under the ISC license. Puzzle is designed to offer reverse image search visually similar images, even after the images have been resized, re -compressed, recolored and/or slightly modified. The "image-match" opensource project was released in 2016. The project, licensed under the Apache License, implements a reverse image search engine written in Python. Both the "Puzzle library" and the "image-match" projects use algorithms published at an IEEE ICIP conference.