Search Engine Optimization for Vector-Based Image Search
Metadata
- Publisher
- SMPTE — Hollywood, CA
- Doc Type
- Conference Paper
- Content Type
- Original Research
- Volume
- 00, pp. 1–11
- Abstract
- Vector-based image search, which represents images and search queries as vectors in a high-dimensional space, performs image search by identifying images whose vector representations closely match the vector representation of the query. Search engine optimization has been traditionally achieved by using relevant keywords to increase organic search engine discoverability. For images to be surfaced as a result of a search query, these relevant keywords have been inserted in the page that hosts the image or embedded directly into an image as metadata that can be decoded and indexed by a text-based search engine. However, when a search engine uses vectors to operate, the static nature of an image's vector representation prevents adaptation of content to increase discoverability. In this paper we present a novel approach to alter an image in a way that enhances its discoverability with a known vector-based search engine while limiting the visual impacts of these alterations for an average user. The alteration is guided by the maximization of intended search queries and the minimization of unwanted search queries.
- Publication Date
- 2024-10-21
- DOI
10.5594/MOO/3046- ISBN
- Electronic:
978-1-61482-965-2 - Link
- https://doi.org/10.5594/MOO/3046
- Author(s)
- Jean-Yves CouleaudMathew AdamsNing Xu
- Keyword(s)
- Vector Search, Image Search Engine Optimization
- Copyright
- © 2024 SMPTE
Bibliographic Reference(s)
- James Han, “How to Build a Visual Search Engine”. In Medium: https://medium.com/analytics-vidhya/how-to-build-a-visual-search-engine-64046e58ad2f EXTERNAL
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- Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus, “Intriguing properties of neural networks”, in International Conference on Learning Representations, 2014. EXTERNAL
- Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, “Universal adversarial perturbations”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1765–1773, 2017. EXTERNAL
- Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z. Berkay Celik, Ananthram Swami, “Practical black-box attacks against machine learning”, in Proceedings of the 2017 A CM on Asia Conference on Computer and Communications Security, pp.506–519. ACM, 2017. EXTERNAL
- Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever, “Learning Transferable Visual Models From Natural Language Supervision”, CoRR 2021. EXTERNAL
- Christian Szegedy, Vincent Vanhoucke, Sergey Loffe, Jonathon Shlens, Zbigniew Wojna, “Rethinking the Inception Architecture for Computer Vision”, CoRR, 2015. EXTERNAL
- Stanislav Fort, “Adversarial examples and their generalization for the OpenAI CLIP model”, https://github.com/stanislavfort/OpenAI_CLIP_adversarial_examples/blob/main/OpenAI_CLIP_adversarial_images_playground.ipynb EXTERNAL
- Aaron Tello-Wharton, “Hackers Guide to Resisting the AI Apocalypse”, in blog.majestic.com, 2021. EXTERNAL
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Jean-Yves Couleaud, Mathew Adams, and Ning Xu; Search Engine Optimization for Vector-Based Image Search, MTS 2024, Article 35 (pp. 1 to 11); SMPTE, 2024, ISBN: 978-1-61482-965-2. Available at https://doi.org/10.5594/MOO/3046
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Jean-Yves Couleaud, Mathew Adams, and Ning Xu; Search Engine Optimization for Vector-Based Image Search, MTS 2024, Article 35 (pp. 1 to 11); SMPTE, 2024, ISBN: 978-1-61482-965-2. Available at https://doi.org/10.5594/MOO/3046
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Jean-Yves Couleaud, Mathew Adams, and Ning Xu; Search Engine Optimization for Vector-Based Image Search, MTS 2024, Article 35 (pp. 1 to 11); SMPTE, 2024, ISBN: 978-1-61482-965-2. Available at https://doi.org/10.5594/MOO/3046
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<span class="citation">Jean-Yves Couleaud, Mathew Adams, and Ning Xu; <cite>Search Engine Optimization for Vector-Based Image Search</cite>, MTS 2024, Article 35 (pp. 1 to 11); SMPTE, 2024, ISBN: 978-1-61482-965-2. Available at <a href="https://doi.org/10.5594/MOO/3046" target="_blank" rel="noopener">https://doi.org/10.5594/MOO/3046</a></span>
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Jean-Yves Couleaud, Mathew Adams, and Ning Xu; Search Engine Optimization for Vector-Based Image Search, MTS 2024, Article 35 (pp. 1 to 11); SMPTE, 2024, ISBN: 978-1-61482-965-2
doi: 10.5594/MOO/3046
url: https://doi.org/10.5594/MOO/3046
doi: 10.5594/MOO/3046
url: https://doi.org/10.5594/MOO/3046
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<li> Jean-Yves Couleaud, Mathew Adams, and Ning Xu; <cite id="bib-10-5594-moo-3046">Search Engine Optimization for Vector-Based Image Search</cite>, MTS 2024, Article 35 (pp. 1 to 11); SMPTE, 2024, ISBN: 978-1-61482-965-2 <span class="doi">10.5594/MOO/3046</span> </li>