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{{Short description|Method of image retrieval}}
[[File:Principe cbir.png|thumb|General scheme of content-based image retrieval]]
'''Content-based image retrieval''', also known as '''query by image content''' ('''[[#QBIC|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 image]]s in large [[database]]s (see this survey<ref name="Survey">''[http://www.ugmode.com/prior_art/lew2006cbm.pdf Content-based Multimedia Information Retrieval: State of the Art and Challenges]''
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"Content-based" means that the search analyzes the contents of the image rather than the [[Metadata (computing)|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 [[Automatic image annotation|annotation]] quality and completeness.
==Comparison with metadata searching==
==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.<ref name="Eakins"/><ref>{{cite journal |last1=Kato |first1=Toshikazu |editor-first1=Albert A. |editor-first2=Carlton W. |editor-last1=Jamberdino |editor-last2=Niblack |title=Database architecture for content-based image retrieval |journal=Image Storage and Retrieval Systems |date=April 1992 |volume=1662 |pages=112–123 |doi=10.1117/12.58497 |bibcode=1992SPIE.1662..112K |publisher=International Society for Optics and Photonics|s2cid=14342247 }}</ref> Since then, the term has been used to describe the process of retrieving desired images from a large collection on the basis of syntactical image features. The techniques, tools, and algorithms that are used originate from fields such as statistics, pattern recognition, signal processing, and computer vision.<ref name="Survey" />
==={{Visible anchor|QBIC}} - Query By Image Content===
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|issue=9
|pages=23–32
}}</ref><ref name="Rui">{{cite journal|last1=Rui|first1=Yong|last2=Huang|first2=Thomas S.|last3=Chang|first3=Shih-Fu|title=Image Retrieval: Current Techniques, Promising Directions, and Open Issues|journal=Journal of Visual Communication and Image Representation|date=1999|volume=10|pages=39–62|doi=10.1006/jvci.1999.0413|citeseerx=10.1.1.32.7819|s2cid=2910032 }}{{dead link|date=September 2017 |bot=InternetArchiveBot |fix-attempted=yes }}</ref> Recent network- and graph
While the storing of multiple images as part of a single entity preceded the term [[Object storage|BLOB]] ('''B'''inary '''L'''arge '''OB'''ject),<ref>{{cite web
|url=http://www.cvalde.net/misc/blob_true_history.htm
|archive-url=https://web.archive.org/web/20110723065224/http://www.cvalde.net/misc/blob_true_history.htm
|url-status=dead
|archive-date=2011-07-23
|title=The true story of BLOBs}}</ref> the ability to fully search by content, rather than by description, had to await IBM's QBIC.<ref name=IW.1996>{{cite magazine |magazine=[[InformationWeek|Information Week]] (OnLine-reprinted in Silicon Investor's Stock Discussion Forums (Aug. 6, 1996) |page=69 (IW) |author=Julie Anderson |date=April 29, 1996 |title=Search Images / Object Design Inc - Bargain of the year Stock Discussion Forums (Aug. 6, 1996) |url=https://www.siliconinvestor.com/readmsgs.aspx?subjectid=6903%26msgnum=17%26batchsize=10%26batchtype=Previous |quote=At DB Expo in San Francisco earlier this month ... }}{{Dead link|date=July 2019 |bot=InternetArchiveBot |fix-attempted=yes }}</ref>
=== VisualRank ===
{{excerpt|VisualRank}}
==Technical progress==
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==Techniques==
Many CBIR systems have been developed, but {{
Different query techniques and implementations of CBIR make use of different types of user queries.
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===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===
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===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>
==Content comparison using image distance measures==
The most common method for comparing two images in content-based image retrieval (typically an example image and an image from the database) is using an image distance measure. An image distance measure compares the [[similarity measure|similarity]] of two images in various dimensions such as color, texture, shape, and others. For example, a distance of 0 signifies an exact match with the query, with respect to the dimensions that were considered. As one may intuitively gather, a value greater than 0 indicates various degrees of similarities between the images. Search results then can be sorted based on their distance to the queried image.<ref name="Shapiro2001" /> Many measures of image distance (Similarity Models) have been developed.<ref>Eidenberger, Horst (2011). "Fundamental Media Understanding", atpress. {{ISBN|978-3-8423-7917-6}}.</ref>
===Color===
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[[Image texture|Texture]] measures look for visual patterns in images and how they are spatially defined. Textures are represented by [[Texel (graphics)|texels]] which are then placed into a number of sets, depending on how many textures are detected in the image. These sets not only define the texture, but also where in the image the texture is located.<ref name="Shapiro2001"/>
Texture is a difficult concept to represent. The identification of specific textures in an image is achieved primarily by modeling texture as a two-dimensional gray level variation. The relative brightness of pairs of pixels is computed such that degree of contrast, regularity, coarseness and directionality may be estimated.<ref name="Rui"/><ref name="Tamura">{{cite journal | last=Tamura| first=Hideyuki |author2=Mori, Shunji |author3=Yamawaki, Takashi | title=Textural Features Corresponding to Visual Perception | journal=IEEE Transactions on Systems, Man, and Cybernetics| year=1978|volume=8|issue=6|pages=460, 473 | doi=10.1109/tsmc.1978.4309999| s2cid=32197839 }}</ref> The problem is in identifying patterns of co-pixel variation and associating them with particular classes of textures such as ''silky'', or ''rough''.
Other methods of classifying textures include:
* [[Image texture#Co-occurrence Matrices|Co-occurrence matrix]]
* [[Image texture#Laws Texture Energy Measures|Laws texture energy]]
* [[Wavelet transform]]
* [[Orthogonal
===Shape===
Shape does not refer to the shape of an image but to the shape of a particular region that is being sought out. Shapes will often be determined first applying [[Segmentation (image processing)|segmentation]] or [[edge detection]] to an image. Other methods use shape filters to identify given shapes of an image.<ref>{{cite book | last=Tushabe | first=F. |author2=M.H.F. Wilkinson | title=Advances in Multilingual and Multimodal Information Retrieval | chapter=Content-
Some shape descriptors include:<ref name="Rui"/>
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== Vulnerabilities, attacks and defenses ==
Like other tasks in [[computer vision]] such as recognition and detection, recent neural network based retrieval algorithms are susceptible to [[generative adversarial network|adversarial attacks]], both as candidate and the query attacks.<ref name="Zhou Niu Wang Zhang 2020">{{cite arXiv | last1=Zhou | first1=Mo | last2=Niu | first2=Zhenxing | last3=Wang | first3=Le | last4=Zhang | first4=Qilin | last5=Hua | first5=Gang | title=Adversarial Ranking Attack and Defense | year=2020 | class=cs.CV | eprint=2002.11293v2 }}</ref> It is shown that retrieved ranking could be dramatically altered with only small perturbations imperceptible to human beings. In addition, model-agnostic transferable adversarial examples are also possible, which enables black-box adversarial attacks on deep ranking systems without requiring access to their underlying implementations.<ref name="Zhou Niu Wang Zhang 2020"/><ref name="Li Ji Liu Hong pp. 4899–4908">{{cite
Conversely, the resistance to such attacks can be improved via adversarial defenses such as the Madry defense.<ref name="Madry Makelov Schmidt Tsipras 2017">{{cite arXiv | last1=Madry | first1=Aleksander | last2=Makelov | first2=Aleksandar | last3=Schmidt | first3=Ludwig | last4=Tsipras | first4=Dimitris | last5=Vladu | first5=Adrian | title=Towards Deep Learning Models Resistant to Adversarial Attacks | date=2017-06-19 | class=stat.ML | eprint=1706.06083v4 }}</ref>
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* Photograph archives
* Retail catalogs
* Nudity-detection filters<ref>{{cite journal | last=Wang |first = James Ze |author2=Jia Li |author2-link=Jia Li|author3=Gio Wiederhold |author4=Oscar Firschein|title=System for Screening Objectionable Images|journal=Computer Communications|year = 1998|volume=21|issue=15|pages=1355–1360|doi=10.1016/s0140-3664(98)00203-5|citeseerx = 10.1.1.78.7689 }}</ref>
* [[Facial recognition system|Face Finding]]
* Textiles Industry<ref name="Bird">{{cite conference | last=Bird | first=C.L. | author2=P.J. Elliott |author3=E. Griffiths | title=User interfaces for content-based image retrieval |book-title=IEE Colloquium on Intelligent Image Databases |publisher=IET |doi=10.1049/ic:19960746 |date=1996}}</ref>
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==Further reading==
{{external links|date=November 2022}}
===Relevant research papers===
* ''[http://doi.ieeecomputersociety.org/10.1109/2.410146 Query by Image and Video Content: The QBIC System]'', (Flickner, 1995)
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* ''[https://doi.org/10.1007%2F3-540-45479-9_17 FACERET: An Interactive Face Retrieval System Based on Self-Organizing Maps]'' (Ruiz-del-Solar et al., 2002)
* ''[http://www-db.stanford.edu/~wangz/project/imsearch/ALIP/PAMI03/ Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach]'' (Li and Wang, 2003)
* ''[
* ''[http://www.svcl.ucsd.edu/publications/journal/2004/sp04/sp04.pdf Minimum Probability of Error Image Retrieval]'' (Vasconcelos, 2004)
* ''[http://www.svcl.ucsd.edu/publications/journal/2004/it04/it04.pdf On the Efficient Evaluation of Probabilistic Similarity Functions for Image Retrieval]'' (Vasconcelos, 2004)
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* ''[http://www-db.deis.unibo.it/research/papers/SIGMAP11.pdf The Windsurf Library for the Efficient Retrieval of Multimedia Hierarchical Data]'' (Bartolini, Patella, and Stromei, 2011)
* "[https://web.archive.org/web/20141129085237/http://identify.plantnet-project.org/en/ Pl@ntNet: Interactive plant identification based on social image data]" (Joly, Alexis et al.)
* "[https://link.springer.com/book/10.1007%2F978-981-10-6759-4 Content based Image Retrieval]'' (Tyagi
* ''[https://dx.doi.org/10.1145/2578726.2578741 Superimage: Packing Semantic-Relevant Images for Indexing and Retrieval]'' (Luo, Zhang, Huang, Gao, Tian, 2014)
* ''[https://dx.doi.org/10.1145/2461466.2461470 Indexing and searching 100M images with Map-Reduce]'' (Moise, Shestakov, Gudmundsson, and Amsaleg, 2013)
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==External links==
* {{cite journal | last=Alkhazraj | first=Huthaefa | title=study for constant-based image relative :A Review | journal=IET Image Processing | volume=IEEE | issue=image processing | date=2017-08-09 | issn=1751-9659 | url=https://www.researchgate.net/publication/319007558 | access-date=2019-01-22}} - the original article
* [https://www.springer.com/13735 IJMIR] many CBIR-related articles
* [http://www.sepham.com/ Search by Drawing]
* [https://web.archive.org/web/20120518124442/http://pixolution.does-it.net/fileadmin/template/visual_web_demo.html Demonstration of a visual search engine for images. (Search by example image or colors)]2.242654
{{DEFAULTSORT:Content-Based Image Retrieval}}
[[Category:Applications of computer vision]]
[[Category:
[[Category:Image search]]
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