Content Based Image Retrieval Using Color Feature Ms. Shilpa P. Pant Computer Department CCOEW Pune Abstract: The purpose of this paper is to describe the problem of designing a Content Based Image Retrieval, CBIR system. Using color feature. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. In CBIR systems, image processing techniques are used to extract visual features such as color, texture and shape from images. Therefore, images are represented as a vector of extracted visual features instead of just pure textual annotations. Color, which represents physical quantities of objects, is an important attribute for image matching and retrieval. Many publications focus on color indexing techniques based on global color distributions. Color correlogram and color coherence [6] vector can combine the spatial correlation of color regions as well as the global distribution of local spatial correlation of colors. These techniques perform better than traditional color histograms when used for content-based image retrieval. However, they require very expensive computation Keywords: CBIR, Feature Extraction, HSV, RGB 1. Introduction Content-based image retrieval (CBIR) [1, 2] is a technique used for extracting similar images from an image database. Due to the advances in digital photography, storage capacity and networks speed, storing large amounts of high quality images has been made possible. Digital images are used in a wide range of applications such as medical, virtual museums, military and security purposes, and personal photo albums. Users have difficulties in organizing and searching large numbers of images in databases. Therefore, an efficient way for image retrieval is desired.In order to respond to this need, researchers have tried extending Information Retrieval (IR) techniques used in text retrieval to the area of image retrieval. In this approach, a set of keywords are assigned to each image. However, there are significant limitations to this approach. First, the approach is not scalable since each object needs to be manually annotated with keywords and/or textual descriptions, making it impractical for large data sets. Second, due to the subjectivity of the human annotator, the annotations may not be consistent or complete which negatively effects retrieval performance. Furthermore, it may be infeasible to describe visual content (e.g., shape of an object) simply using words 1.1 Content Based Image Retrieval CBIR or Content Based Image Retrieval is the retrieval of images based on visual features such as colour, texture and shape Reasons for its development are that in many large image databases, traditional methods of image indexing have proven to be insufficient, laborious, and extremely time consuming[2]. These old methods of image indexing, ranging from storing an image in the database and associating it with a keyword or number, to associating it with a categorized description, have become obsolete. This is not CBIR. In CBIR, each image that is stored in the database has its features extracted and compared to the features of the query image. There are many things to consider in the design of a system for content-based search in image databases: Image features – What visual features are most useful in each particular case? Image representation – How should we code the image features? Representation storage and retrieval – The search must be made fast. What are the proper searching techniques and indexing structures? User interface – How should the user best browse and search for images? Figure 1-1. Diagram for content-based image retrieval system The main steps in CBIR are: Feature Extraction – Features are extracted from the images. The definitions of features are usually pre-defined, such as color, texture and shape. These features are usually stored in the form of real-valued multi- dimensional vectors. International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 4, April - 2013 ISSN: 2278-0181 www.ijert.org 398