IJREAT International Journal of Research in Engineering & Advanced Technology, Volume 2, Issue 1, Feb-Mar, 2014 ISSN: 2320 - 8791 www.ijreat.org www.ijreat.org Published by: PIONEER RESEARCH & DEVELOPMENT GROUP (www.prdg.org) 1 Abstract— Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and annotated image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So nowadays the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. In recent years, a variety of techniques have been developed to improve the performance of CBIR. An image retrieval system that takes the input query image and retrieves the similar images according to the spatial coordinates and which uses the k means clustering algorithm for its segmentation. Most existing Content Based Image Retrieval based on the images of color, text documents, informative charts, and shape. Keywords— Content Based Image Retrieval, Clustering Techniques, Color, Texture, and Shape. I. INTRODUCTION Content-Based Image Retrieval (CBIR) is defined as a process that searches and retrieves images from a large database on the basis of automatically-derived features such as color, texture and shape. The techniques, tools and algorithms that are used in CBIR, originate from many fields. Such as statistics, pattern recognition, signal processing, and computer vision. It is a field of research that is attracting professionals from different industries like crime prevention, medicine, architecture, fashion and publishing. 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 database. "Content-based" means that the search analyses the contents of the image rather than the metadata such as keywords, tags, 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 is desirable because most web- based image search engines rely purely on metadata and this produces a lot of garbage in the results. Also having humans manually enter keywords for images in a large database can be inefficient, expensive and may not capture every keyword that describes the image. Thus a system that can filter images based on their content would provide better indexing and return more accurate results.[1][2][3] Figure 1: A Conceptual Framework for Content-Based Image Retrieval. II. THE RETRIEVAL OF CONTENT BASED IMAGE A. Color-based retrieval Out of the many feature extraction techniques, color is considered as the most dominant and distinguishing visual feature. Generally, it adopts histograms to describe it. A color histogram describes the global color distribution in an image and is more frequently used technique for content-based image retrieval (Wang and Qin, 2009) because of its efficiency and effectiveness. Color histograms method has the advantages of speediness, low demand of memory space Study on Query Based Clustering Technique for Content Based Image Retrieval Vinita Kushwah 1 , Arun Agrawal 2 1 Research Scholar, ITM, Gwalior 2 Dept. of CSE, Assistant Professor, ITM, Gwalior