API Build-data JSON Resources
Theme

Choose how MSRBot.io looks on this device.

Preference is stored in this browser only.

MIJ 2025, Volume 134, Number 5 (pp. 20 to 26)
[ACTIVE]

Search Engine Image Optimization

Metadata

Publisher
SMPTE
Doc Type
Journal Article
Content Type
Original Research
Abbreviated Title
SMPTE Motion Imaging Jour.
Volume
134, No. 5, pp. 20–26
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 into 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 minimizing the visual impact of these alterations for the average user. The alteration is guided by the maximization of intended search queries and the minimization of unwanted search queries.
Publication Date
2025-09-01
DOI
10.5594/JMI.2025/FVEV7398
ISSN
Print: 1545-0279 | Electronic: 2160-2492
Link
https://doi.org/10.5594/JMI.2025/FVEV7398
Author(s)
Jean-Yves Couleaud
Mathew Adams
Ning Xu
Keyword(s)
Vector Search, Image Search Engine Optimization
Copyright
© 2025 SMPTE
Source Data (JSON)

Full registry record with provenance metadata. Open directly: /api/doc/10.5594-JMI.2025-FVEV7398.json

Reference this Doc

Plain text (ISO 690 compliant)

Preview:
Jean-Yves Couleaud, Mathew Adams, and Ning Xu; Search Engine Image Optimization, MIJ 2025, Volume 134, Number 5 (pp. 20 to 26); SMPTE, 2025. Available at https://doi.org/10.5594/JMI.2025/FVEV7398
Snippet:
Jean-Yves Couleaud, Mathew Adams, and Ning Xu; Search Engine Image Optimization, MIJ 2025, Volume 134, Number 5 (pp. 20 to 26); SMPTE, 2025. Available at https://doi.org/10.5594/JMI.2025/FVEV7398

HTML (ISO 690 compliant)

Preview:
Jean-Yves Couleaud, Mathew Adams, and Ning Xu; Search Engine Image Optimization, MIJ 2025, Volume 134, Number 5 (pp. 20 to 26); SMPTE, 2025. Available at https://doi.org/10.5594/JMI.2025/FVEV7398
Snippet:
<span class="citation">Jean-Yves Couleaud, Mathew Adams, and Ning Xu; <cite>Search Engine Image Optimization</cite>, MIJ 2025, Volume 134, Number 5 (pp. 20 to 26); SMPTE, 2025. Available at <a href="https://doi.org/10.5594/JMI.2025/FVEV7398" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2025/FVEV7398</a></span>

SMPTE Icon SMPTE's HTML Pub

Preview:
Jean-Yves Couleaud, Mathew Adams, and Ning Xu; Search Engine Image Optimization, MIJ 2025, Volume 134, Number 5 (pp. 20 to 26); SMPTE, 2025
doi: 10.5594/JMI.2025/FVEV7398
url: https://doi.org/10.5594/JMI.2025/FVEV7398
Snippet:
<li>
Jean-Yves Couleaud, Mathew Adams, and Ning Xu; <cite id="bib-10-5594-jmi-2025-fvev7398">Search Engine Image Optimization</cite>, MIJ 2025, Volume 134, Number 5 (pp. 20 to 26); SMPTE, 2025
<span class="doi">10.5594/JMI.2025/FVEV7398</span>
</li>