Query Image Retrieval From Content Management System
Content Based Image Retrieval (CBIR) techniques appeared in 1990s. It uses low-level features like color,
texture and shape to describe image content, and breaks through the limitation of traditional text query technique. This
project we are proposes an image retrieval from the content management system (CMS), method based on color-feature and
texture-feature similarity score. Many methods can be used to describe color feature. In this project we will use color
moment method because it is the lowest feature vector dimensions and lower computational complexity. HSV/HSB color
space is describes a specific color by its hue, saturation, and values/ brightness. For the similarity measurement the first order
mean, the second standard deviation, and the third skewness color moments have been provided to be efficient and effective
in representing color distribution of images.
In this paper, the retrieval results from color feature and texture feature are analyzed, furthermore texture can be through as
repeated patterns of pixel over a spatial domain. Since there is no mathematical definition for texture, many different
methods are proposed for computing texture. Here we are used different distance based similarity measurement methods are
proposed, before we are doing texture features apply Ranklet transform can be calculated at different resolutions using Haar
Keywords— Content Based Image Retrieval (CBIR), Hue, Saturation, and Values (HSV), Hue, Saturation, and brightness
(HSB), Color feature, Texture Feature, Ranklet Transform.