Abstract
Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. “Content-based" means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and/or descriptions associated with the image. The term 'content' in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself.CBIR extracts low-level features which is inbuilt in the images to present the contents of images. Each image has Visual features such as classified into three main classes: color ,texture and shape features. Color is an important image feature such as used in Content-Based Image Retrieval. K-Means is a clustering method based on the optimization of an overall measure of clustering quality is known for its efficiency in producing accurate results in image retrieval. K-Means technique with all the images in the database. The number of similarity comparisons required depends on the sizes of the clusters and the number of clusters being examined.