several color regions. 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. 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: Co-occurrence matrix Laws texture energy Wavelet transform Orthogonal transforms (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 or edge detection to an image. Other methods use shape filters to identify given shapes of an image. Shape descriptors may also need to be invariant to translation, rotation, and scale. Some shape descriptors include: Fourier transform Moment invariant 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. 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. Conversely, the resistance to such attacks can be improved via adversarial defenses such as the Madry defense. Image retrieval evaluation Measures of image retrieval can be defined in terms of precision and recall. However, there are other methods being considered. 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. Applications Potential uses for CBIR include: Architectural and engineering design Art collections Crime prevention Geographical information and remote sensing systems Intellectual property Medical diagnosis Military Photograph archives Retail catalogs Nudity-detection filters Face