Editing Content-based image retrieval (section)

==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===
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.<ref name="Eakins"/> 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.<ref name="Rui"/>  However, research has also attempted to segment color proportion by region and by spatial relationship among several color regions.<ref name="Mayron"/>

===Texture===
[[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 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-Based 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"/>
* [[Fourier transform]]
* [[Image moment|Moment invariant]]