CACrown ArchivesThe cinema collection
Menu
Research dossier · General Reference

Content-based image retrieval

method of image retrieval

Cross-disciplinary reference desk with index cards, atlas, dictionary and catalogue
General referenceInterpretive dossier study · Crown Archives visual atlas
Record originEnglish Wikipedia
Text licenseCC BY-SA 4.0
Source revisionAug 16, 2026
Entity authorityQ1128648
Source-derived summary

Content-based image retrieval, also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey for a scientific overview of the CBIR field). Content-based image retrieval is opposed to traditional concept-based approaches (see Concept-based image indexing).

"Content-based" means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image. The term "content" in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because searches that rely purely on metadata are dependent on annotation quality and completeness.

Comparison with metadata searching

An image meta search requires humans to have manually annotated images by entering keywords or metadata in a large database, which can be time-consuming and may not capture the keywords desired to describe the image. The evaluation of the effectiveness of keyword image search is subjective and has not been well-defined. In the same regard, CBIR systems have similar challenges in defining success. "Keywords also limit the scope of queries to the set of predetermined criteria." and, "having been set up" are less reliable than using the content itself.

History

The term "content-based image retrieval" seems to have originated in 1992 when it was used by Japanese Electrotechnical Laboratory engineer Toshikazu Kato to describe experiments into automatic retrieval of images from a database, based on the colors and shapes present.

Editorial summary

This brief starts where responsible research should: with the source description of “Content-based image retrieval” as method of image retrieval. Everything that follows is an evidence route, not borrowed authority.

Editorial reviewA concise reference frame for defining the subject, testing terminology and identifying the institution closest to the evidence. The current lead gives the account dated anchors—1992—that can be checked directly. The selected authority fields contribute no independent date. The account is most persuasive where Content-based, image and retrieval can be independently traced.
Editorial analysis

Why this record matters

The subject matters to the general reference register because the source frames it as method of image retrieval. Its deeper value depends on whether names, dates, institutions and citations support that framing.

Evidence profile

Named sources, stable identifiers and responsible institutions provide the strongest route from overview to verifiable evidence. The source revision retrieved here is dated Aug 16, 2026. The linked authority identifier is Q1128648. None of the 0 selected statements returned an explicit reference. The first chronological checks are 1992.

Critical limits

A concise general-reference account can conceal disagreements about scope, terminology or the weight assigned to individual sources. The source lead contains qualifying language; that uncertainty should survive quotation, summary and reuse. Authority statements aid reconciliation but still require their own references, qualifiers and ranks to be checked.

How to read it

Use the entry as an orientation point, then follow its citations and revision history. Names, dates and institutional relationships should be checked against the original record.

Best used for
  • Subject orientation
  • Search vocabulary
  • Locating named sources
Verify next

The closest primary source, responsible institution and strongest cited specialist reference.

Three-step research path

  1. Establish the record: confirm the title “Content-based image retrieval”, its source revision and the description used here.
  2. Expand the search: follow Content-based image retrieval primary sources, Content-based image retrieval archive and Content-based research across catalogues and specialist indexes.
  3. Test the account: compare the strongest cited source with the responsible institution’s current record and note any disagreement.

Questions for further research

  1. Which source most directly establishes the central claim about “Content-based image retrieval”?
  2. Which institution is responsible for the underlying evidence?
  3. Which cited source is closest to the event, object or claim?
Subject index

Search terms from this dossier

Source & attribution

This entry incorporates text from Content-based image retrieval” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.