Content-based image retrieval

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Short description: Method of image retrieval
General scheme of content-based image retrieval

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[1] 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.[2] "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.[3]

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.[2][4] 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.[1]

QBIC - Query By Image Content

The earliest commercial CBIR system was developed by IBM and was called QBIC (Query By Image Content).[5][6] Recent network- and graph-based approaches have presented a simple and attractive alternative to existing methods.[7]

While the storing of multiple images as part of a single entity preceded the term BLOB (Binary Large OBject),Cite error: Closing </ref> missing for <ref> tag 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.[8]

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.[8]

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. 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.[6]

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.[9] 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.[10]

Iterative/machine learning

Machine learning and application of iterative techniques are becoming more common in CBIR.[11]

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 queries (e.g. combining touch, voice, etc.)[12]

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 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.[8] Many measures of image distance (Similarity Models) have been developed.[13]

Color

Computing distance measures based on color similarity is achieved by computing a color histogram for each image that identifies the proportion of pixels within an image holding specific values.[2] Examining images based on the colors they contain is one of the most widely used techniques because it can be completed without regard to image size or orientation.[6] However, research has also attempted to segment color proportion by region and by spatial relationship among several color regions.[12]

Texture

Texture measures look for visual patterns in images and how they are spatially defined. Textures are represented by 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.[8]

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.[6][14] 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:

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 or edge detection to an image. Other methods use shape filters to identify given shapes of an image.[15] Shape descriptors may also need to be invariant to translation, rotation, and scale.[6]

Some shape descriptors include:[6]

Vulnerabilities, attacks and defenses

Like other tasks in computer vision such as recognition and detection, recent neural network based retrieval algorithms are susceptible to adversarial attacks, both as candidate and the query attacks.[16] 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.[16][17]

Conversely, the resistance to such attacks can be improved via adversarial defenses such as the Madry defense.[18]

Image retrieval evaluation

Measures of image retrieval can be defined in terms of precision and recall. However, there are other methods being considered.[19]

Image retrieval in CBIR system simultaneously by different techniques

An image is retrieved in CBIR system by adopting several techniques simultaneously such as Integrating Pixel Cluster Indexing, histogram intersection and discrete wavelet transform methods.[20]

Applications

Potential uses for CBIR include:[2]

Commercial Systems that have been developed include:[2]

  • IBM's QBIC
  • Virage's VIR Image Engine
  • Excalibur's Image RetrievalWare
  • VisualSEEk and WebSEEk
  • Netra
  • MARS
  • Vhoto
  • Pixolution

Experimental Systems include:[2]

  • MIT's Photobook
  • Columbia University's WebSEEk
  • Carnegie-Mellon University's Informedia
  • iSearch - PICT

See also

References

  1. ↑ 1.0 1.1 Content-based Multimedia Information Retrieval: State of the Art and Challenges (Original source, 404'd)Content-based Multimedia Information Retrieval: State of the Art and Challenges , Michael Lew, et al., ACM Transactions on Multimedia Computing, Communications, and Applications, pp. 1–19, 2006.
  2. ↑ 2.0 2.1 2.2 2.3 2.4 2.5 Eakins, John; Graham, Margaret. "Content-based Image Retrieval". University of Northumbria at Newcastle. http://www.jisc.ac.uk/uploaded_documents/jtap-039.doc. 
  3. ↑ Cite error: Invalid <ref> tag; no text was provided for refs named IW.1996
  4. ↑ Kato, Toshikazu (April 1992). "Database architecture for content-based image retrieval". Image Storage and Retrieval Systems (International Society for Optics and Photonics) 1662: 112–123. doi:10.1117/12.58497. Bibcode: 1992SPIE.1662..112K. 
  5. ↑ Flickner, M.; Sawhney, H.; Niblack, W.; Ashley, J.; Qian Huang; Dom, B.; Gorkani, M.; Hafner, J. et al. (1995). "Query by image and video content: the QBIC system". Computer 28 (9): 23–32. doi:10.1109/2.410146. "Abstract: Research on ways to extend and improve query methods for image databases is widespread. We have developed the QBIC (Query by Image Content) ...". 
  6. ↑ 6.0 6.1 6.2 6.3 6.4 6.5 Rui, Yong; Huang, Thomas S.; Chang, Shih-Fu (1999). "Image Retrieval: Current Techniques, Promising Directions, and Open Issues". Journal of Visual Communication and Image Representation 10: 39–62. doi:10.1006/jvci.1999.0413. [yes|permanent dead link|dead link}}]
  7. ↑ Banerjee, S. J. (2015). "Using complex networks towards information retrieval and diagnostics in multidimensional imaging". Scientific Reports 5: 17271. doi:10.1038/srep17271. PMID 26626047. Bibcode: 2015NatSR...517271B. 
  8. ↑ 8.0 8.1 8.2 8.3 Shapiro, Linda; George Stockman (2001). Computer Vision. Upper Saddle River, NJ: Prentice Hall. ISBN 978-0-13-030796-5. 
  9. ↑ Datta, Ritendra; Dhiraj Joshi; Jia Li; James Z. Wang (2008). "Image Retrieval: Ideas, Influences, and Trends of the New Age". ACM Computing Surveys 40 (2): 1–60. doi:10.1145/1348246.1348248. http://infolab.stanford.edu/~wangz/project/imsearch/review/JOUR/. 
  10. ↑ 10.0 10.1 Bird, C.L.; P.J. Elliott; E. Griffiths (1996). "User interfaces for content-based image retrieval". IET. doi:10.1049/ic:19960746. 
  11. ↑ Cardoso, Douglas. "Iterative Technique for Content-Based Image Retrieval using Multiple SVM Ensembles". Federal University of Parana(Brazil). http://iris.sel.eesc.usp.br/wvc/Anais_WVC2013/Oral/1/6.pdf. 
  12. ↑ 12.0 12.1 Liam M. Mayron. "Image Retrieval Using Visual Attention". Mayron.net. http://mayron.net/liam/pub/mayron_dissertation.pdf. 
  13. ↑ Eidenberger, Horst (2011). "Fundamental Media Understanding", atpress. ISBN:978-3-8423-7917-6.
  14. ↑ Tamura, Hideyuki; Mori, Shunji; Yamawaki, Takashi (1978). "Textural Features Corresponding to Visual Perception". IEEE Transactions on Systems, Man, and Cybernetics 8 (6): 460, 473. doi:10.1109/tsmc.1978.4309999. 
  15. ↑ Tushabe, F.; M.H.F. Wilkinson (2008). "Content-Based Image Retrieval Using Combined 2D Attribute Pattern Spectra". Advances in Multilingual and Multimodal Information Retrieval. Lecture Notes in Computer Science. 5152. pp. 554–561. doi:10.1007/978-3-540-85760-0_69. ISBN 978-3-540-85759-4. https://pure.rug.nl/ws/files/2720522/2008LNCSTushabe.pdf. 
  16. ↑ 16.0 16.1 Zhou, Mo; Niu, Zhenxing; Wang, Le; Zhang, Qilin; Hua, Gang (2020). "Adversarial Ranking Attack and Defense". arXiv:2002.11293v2 [cs.CV].
  17. ↑ Li, Jie; Ji, Rongrong; Liu, Hong; Hong, Xiaopeng; Gao, Yue; Tian, Qi (2019). "Universal Perturbation Attack Against Image Retrieval". pp. 4899–4908. arXiv:1812.00552 [cs.CV].
  18. ↑ Madry, Aleksander; Makelov, Aleksandar; Schmidt, Ludwig; Tsipras, Dimitris; Vladu, Adrian (2017-06-19). "Towards Deep Learning Models Resistant to Adversarial Attacks". arXiv:1706.06083v4 [stat.ML].
  19. ↑ Deselaers, Thomas; Keysers, Daniel; Ney, Hermann (2007). "Features for Image Retrieval: An Experimental Comparison". RWTH Aachen University. http://thomas.deselaers.de/publications/papers/deselaers_infret08.pdf. 
  20. ↑ Bhattacharjee, Pijush kanti (2010). "Integrating Pixel Cluster Indexing, Histogram Intersection and Discrete Wavelet Transform Methods for Color Images Content Based Image Retrieval System". http://www.ijcee.org/papers/159.pdf. 
  21. ↑ Wang, James Ze; Jia Li; Gio Wiederhold; Oscar Firschein (1998). "System for Screening Objectionable Images". Computer Communications 21 (15): 1355–1360. doi:10.1016/s0140-3664(98)00203-5. 

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