Color is the Most Demonstrative Visual Feature and Studied in the Context of CBIR

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Color is widely remarked as one of the most demonstrative visual features, and as such it has been largely studied in the context of CBIR, thus number one to a rich variety of descriptors. As traditional color features used in CBIR, there are color histogram, color correlogram, and dominant color descriptor (DCD) [1,3,4]. A simple color similarity between two images can be measured by comparing their color histograms. The color histogram, which is a common color descriptor, indicates the occurrence frequencies of colors in the image. The color correlogram describes the probability of finding color pairs at a fixed pixel distance and provides spatial information. Therefore color correlogram yields better retrieval accuracy in comparisonto …show more content…

Texture is also an important visual feature that refers to innate surface properties of an object and their relationship to the surrounding environment. Many objects in an image can be distinguished solely by their textures without any other information. In conventional texture features used for CBIR, there are statistic texture features using gray-level co-occurrence matrix (GLCM), Markov random field (MRF) model, simultaneous auto-regressive (SAR) model, Wold decomposition model, edge histogram descriptor (EHD), etc. Recently, BDIP (block difference of inverse probabilities) and BVLC (block variation of local correlation coefficients) features have been proposed which effectively measure local brightness variations and local texture smoothness, respectively [9]. These features are shown to yield better retrieval accuracy over the compared conventional features. Kokare et al. [10] designed a new set of 2D rotated wavelet by using Daubechies eight tap coefficients to improve the image retrieval accuracy. The 2D rotated wavelet filters that are non-separable and oriented, improves characterization of diagonally oriented textures. In Ref. [11], He et al. presented a novel method, which uses non-separable wavelet filter banks, to extract the features of texture images for texture image retrieval. Compared to traditional tensor product wavelets (such as db wavelets), the new method can capture more

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