Editing Content-based image retrieval (section)

==Techniques==
Many CBIR systems have been developed, but {{as of|2006|lc=y}}, the problem of retrieving images on the basis of their pixel content remains largely unsolved.<ref name="Survey"/>{{update inline|date=January 2020}}

Different query techniques and implementations of CBIR make use of different types of user queries.

===Query By Example===
'''QBE''' ([[Query by example|'''Q'''uery '''B'''y '''E'''xample]]) is a query technique<ref>{{cite web
|quote=QBE is a language for querying ...
|url=https://www.ibm.com/support/knowledgecenter/en/SS9UMF_11.2.1/ugr/ugr/tpc/dsq_query_by_ex.html
|title=Query-by-Example |website=IBM.com KnowledgeCenter}}</ref> that involves providing the CBIR system with an example image that it will then base its search upon.  The underlying search algorithms may vary depending on the application, but result images should all share common elements with the provided example.<ref name="Shapiro2001" />{{See also|Reverse image search}}

Options for providing example images to the system include:
* A preexisting image may be supplied by the user or chosen from a random set.
* The user draws a rough approximation of the image they are looking for, for example with blobs of color or general shapes.<ref name="Shapiro2001">{{cite book | last=Shapiro | first=Linda |author-link=Linda Shapiro|author2=George Stockman | title=Computer Vision | isbn=978-0-13-030796-5 | year=2001 | publisher=Prentice Hall | location=Upper Saddle River, NJ }}</ref>

This query technique removes the difficulties that can arise when trying to describe images with words.

===Semantic retrieval===
''Semantic'' retrieval starts with a user making a request like "find pictures of Abraham Lincoln". This type of open-ended task is very difficult for computers to perform - Lincoln may not always be facing the camera or in the same [[pose (computer vision)|pose]]. Many CBIR systems therefore generally make use of lower-level features like texture, color, and shape.  These features are either used in combination with interfaces that allow easier input of the criteria or with databases that have already been trained to match features (such as faces, fingerprints, or shape matching).  However, in general, image retrieval requires human feedback in order to identify higher-level concepts.<ref name="Rui" />

===Relevance feedback (human interaction)===
Combining CBIR search techniques available with the wide range of potential users and their intent can be a difficult task.  An aspect of making CBIR successful relies entirely on the ability to understand the user intent.<ref name="Ddata">{{cite journal | last=Datta | first=Ritendra |author2=Dhiraj Joshi |author3=Jia Li|author3-link=Jia Li |author4=James Z. Wang  | title=Image Retrieval: Ideas, Influences, and Trends of the New Age | journal=ACM Computing Surveys | url=http://infolab.stanford.edu/~wangz/project/imsearch/review/JOUR/ | year=2008 | doi=10.1145/1348246.1348248 | volume=40 | issue=2 | pages=1–60| s2cid=7060187 }}</ref>  CBIR systems can make use of ''[[relevance feedback]]'', where the user progressively refines the search results by marking images in the results as "relevant", "not relevant", or "neutral" to the search query, then repeating the search with the new information.  Examples of this type of interface have been developed.<ref name="Bird"/>

===Iterative/machine learning===
[[Machine learning]] and application of iterative techniques are becoming more common in CBIR.<ref name="Cardoso">{{cite web|url=http://iris.sel.eesc.usp.br/wvc/Anais_WVC2013/Oral/1/6.pdf |title=Iterative Technique for Content-Based Image Retrieval using Multiple SVM Ensembles |author=Cardoso, Douglas |publisher=Federal University of Parana(Brazil) |access-date=2014-03-11|display-authors=etal}}</ref>

===Other query methods===
Other query methods include browsing for example images, navigating customized/hierarchical categories, querying by image region (rather than the entire image), querying by multiple example images, querying by visual sketch, querying by direct specification of image features, and [[multimodal interaction|multimodal]] queries (e.g. combining touch, voice, etc.)<ref name="Mayron">{{cite web|url=http://mayron.net/liam/pub/mayron_dissertation.pdf |title=Image Retrieval Using Visual Attention |author=Liam M. Mayron |publisher=Mayron.net |access-date=2012-10-18}}</ref>