Content-based image retrieval: Difference between revisions

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}}</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-based approaches have presented a simple and attractive alternative to existing methods.<ref name="Banerjee">{{cite journal|last1=Banerjee, S. J.|display-authors=et al|title=Using complex networks towards information retrieval and diagnostics in multidimensional imaging|journal=Scientific Reports|date=2015|volume=5|pages=17271|doi=10.1038/srep17271|arxiv=1506.02602|pmid=26626047|pmc=4667282|bibcode=2015NatSR...517271B}}</ref>

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

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===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 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 transform]]s (discrete Chebyshev moments)

===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-basedBased Image Retrieval Using Combined 2D Attribute Pattern Spectra | volume=5152 | pages=554–561 | year=2008| doi=10.1007/978-3-540-85760-0_69 | series=Lecture Notes in Computer Science | isbn=978-3-540-85759-4 | s2cid=18566543 | url=https://pure.rug.nl/ws/files/2720522/2008LNCSTushabe.pdf }}</ref> Shape descriptors may also need to be invariant to translation, rotation, and scale.<ref name="Rui"/>

Some shape descriptors include:<ref name="Rui"/>

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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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* ''[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)

* ''[httphttps://ieeexplore.ieee.org/iel5stamp/8769/27772/01238663stamp.pdfjsp?arnumber=1238663 Video google: A text retrieval approach to object matching in videos]'' (Sivic & Zisserman, 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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* [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

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