Reverse image search

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 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.[6] This procedure is able to match even very edited versions of the submitted image, but will not usually return similar images in the results.[7]

Pixsy reverse image search technology detects image matches[8] on the public internet for images uploaded to the Pixsy platform.[9] New matches are automatically detected and alerts sent to the user. For unauthorised use, Pixsy offers a compensation recovery service[10][11] 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 user.[12]

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.[13]

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.[14]

Alibaba released the Pailitao application during 2014. 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 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.[15][16]

Pinterest acquired startup company VisualGraph in 2014 and introduced visual search on its platform.[17] 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.[18][19]

LykDat uses reverse image search to find fashion products across various online stores on the web.[20] LykDat also provides a Twitter bot that helps users carry out reverse image searches of photos they find within Twitter.[21]

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.[22]