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CBIR field started to include human centered design that tried to meet the needs of the user performing the search.This typically means inclusion of query methods that may allow descriptive semantics, queries that may involve user feedback, systems that may include machine learning, and systems that may understand user satisfaction levels.TechniqueseditMany CBIR systems have been developed, but the problem of retrieving images on the basis of their pixel content remains largely unsolved.Query techniqueseditDifferent implementations of CBIR make use of different types of user queries.Query by example is a query technique 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.Options for providing example images to the system include A preexisting image may be supplied by the user or chosen from 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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.Many measures of image distance Similarity Models have been developed.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.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.However, research has also attempted to segment color proportion by region and by spatial relationship among several color regions.TextureeditTexture 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.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.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 does 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