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    Reverse Image Search: Unlocking the Secrets of Visual Recognition
    Reverse Image Search: Unlocking the Secrets of Visual Recognition
    Reverse Image Search: Unlocking the Secrets of Visual Recognition
    Ebook81 pages48 minutesComputer Vision

    Reverse Image Search: Unlocking the Secrets of Visual Recognition

    By Fouad Sabry

    ()

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    About this ebook

    What is 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 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 or the popularity of an image, and to discover manipulated versions and derivative works.


    How you will benefit


    (I) Insights, and validations about the following topics:


    Chapter 1: Reverse image search


    Chapter 2: Web crawler


    Chapter 3: Image retrieval


    Chapter 4: Recommender system


    Chapter 5: Document retrieval


    Chapter 6: Content-based image retrieval


    Chapter 7: Automatic image annotation


    Chapter 8: Inverted index


    Chapter 9: Google Images


    Chapter 10: Social search


    (II) Answering the public top questions about reverse image search.


    (III) Real world examples for the usage of reverse image search in many fields.


    Who this book is for


    Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Reverse Image Search.

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateMay 5, 2024
    Reverse Image Search: Unlocking the Secrets of Visual Recognition

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      Book preview

      Reverse Image Search - Fouad Sabry

      Chapter 1: Reverse image search

      The sample image is very useful for information retrieval purposes in reverse image search, which is a content-based image retrieval (CBIR) query technique that involves providing the CBIR system with an image to base its search on. The absence of search terms is especially noticeable in reverse image search. As a result, the user is no longer required to blindly enter keywords or terms in the hope that they will yield the desired results. Users can use reverse image search to find results that are relevant to an uploaded image, Utilizing a reverse image search could:

      Find out where a picture was taken.

      Obtain better quality images.

      Locate the URLs of pages that feature the image.

      Locate the source of the content.

      Find out more about a picture you've seen.

      Algorithms for performing a reverse search on an image include:

      Image local feature extraction using a scale-invariant feature transform

      Maximum stability at the ends

      Vocabulary tree

      Yandex Images provides a reverse image and photo search for the entire world. In addition to the common Content Based Image Retrieval (CBIR) technology, the site also employs artificial intelligence-based technology to find related results based on the user's query. Users can search the web for other images that are similar to the one they dragged and dropped into the site's toolbar. Yandex Images searches not only the most popular social media sites, but also some lesser-known ones, giving content owners a way to monitor the spread of stolen images and photos.

      By uploading an image or pasting the image's URL, users can conduct a reverse image search using Google's Search by Image feature. Google is able to do this because it examines the submitted image and creates a mathematical model of it. The image is then compared to those already stored in Google's database to determine if any matches exist. Google also makes use of image metadata like description when it is available. Even though Google Lens has taken over as the platform's primary visual search tool as of 2022, the older Search by Image feature is still accessible from within Lens.

      TinEye is an image search engine that works in reverse. In order to compare submitted images to those already in its database, TinEye generates a unique and compact digital signature or fingerprint for each image.

      Pixsy is a reverse image search engine that can find similar images.

      The eBay ShopBot can search for items in an uploaded image by using reverse image search. For category recognition, eBay employs a ResNet-50 network; Google Bigtable is used to store image hashes; Apache Spark jobs are managed by Google Cloud Dataproc; and Kubernetes is used to deploy eBay's image ranking service.

      SK Planet's e-commerce website can perform a reverse image search to locate similar clothing items. TensorFlow inception-v3 was used to build a vision encoder network optimized for speed of convergence and generalization in production settings. Faster R-CNN is used for region-of-interest detection in the fashion industry, and a recurrent neural network is used for multi-class classification. In less than a hundred man-months, SK Planet was able to develop a reverse image search system.

      Alibaba's Pailitao app first appeared in 2014.

      Pailitao (Chinese: 拍立淘, The feature, which translates to shopping with a camera, enables users to conduct product searches on Alibaba's electronic commercial platform by snapping a picture of the desired item.

      With a deep CNN model with branches for joint detection and feature learning, the Pailitao app is able to isolate the detection mask and precise discriminative feature from noise.

      For both category prediction and feature learning, GoogLeNet V1 is used as the foundational model.

      In 2014, Pinterest purchased visual search startup VisualGraph and integrated the feature into its own product.

      At the Middleware '18 conference, JD.com unveiled the inner workings of its real-time visual search system. The algorithms used by JD's 300 million daily active users' distributed hierarchical image feature extraction, indexing, and retrieval system are the focus of the peer reviewed paper. In 2018, when the system went live, it was able

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