International Journal of Computer Applications (0975 – 8887) Volume 183 – No. 25, September 2021 37 Comprehensive Review of Content based Image Retrieval Shital Jadhav Assistant Professor Department of CSE-IT GHRIBM, Jalgaon Sonal Patil Assistant Professor Department of CSE-IT GHRIBM, Jalgaon Hiralal Solunke Assistant Professor Department of CSE-IT GHRIBM, Jalgaon ABSTRACT Since the last decade, Content-Based Image Retrieval was a most exciting and fastest growing research area. The computational complexity and theretrieval accuracy are the main problems that CBIR systems have to avoid. To avoid these problems, this paper proposes a new content-based image retrieval method that uses color, texture and edge direction feature . Color features are the fundamental characteristics of the content of images. Color feature is one of the most widely used features in low level feature. Texture provides the measures of properties such as smoothness, coarseness, and regularity. The edge of the image is another important feature that represented the content of the image. Using color,texture and edge direction feature to describe the image and use them for image retrieval is more accurate than using one of them. . General Terms Content Based Image Retrieval (CBIR) Keywords Content-Based Image Retrieval (CBIR), Color Moment, Texture, Local Binary Pattern(LBP),Edge Histogram 1. INTRODUCTION Application of World Wide Web (www) and the internet is increasing exponentially, and with it the amount of digital image data accessible to the users Due to development in technology uses of smart phone and digital camera,a huge amount of Image databases are added every minute and so is the need for effective and efficient image retrieval systems. There are many features of content-based image retrieval but four of them are considered to be the main features. They are color, texture, shape, and spatial properties. Spatial properties, however, are implicitly taken into account so the main features to investigate are color, texture and shape[1]. In small collection of images it is easy to retrieve an image.But in large and varied variety of images, it is very difficult to retrieve an image.Image retrieval is a problem of searching and retrieving an image according to user request from database.[10] Content Based Image Retrieval (CBIR) is the retrieval of mages based on their visual features such as color, texture, and shape. Content-based image retrieval systems have become a reliable tool for many image database applications. There are several advantages of image retrieval techniques compared to other simple retrieval approaches such as text- based retrieval techniques CBIR provides a solution for many types of image information management systems such as medical imagery, criminology, and satellite imagery. In this computer age, virtually all spheres of human life including commerce, government, academics, hospitals, crime prevention, surveillance, engineering, architecture, journalism, fashion and graphic design, and historical research use images for efficient services. A large collection of images is referred to as image database. An image database is a system where image data are integrated and stored . Image data include the raw images and information extracted from images by automated or computer assisted image analysis[2]. A typical CBIR uses the contents of an image to represent and access. CBIR systems extract features (color, texture, and shape) from images in the database based on the value of the image pixels. These features are smaller than the image size and stored in a database called feature database. Thus the feature database contains an abstraction (compact form) of the images in the image database; each image is represented by a compact representation of its contents (color, texture, shape, and spatial information) in the form of a fixed length real- valued multicomponent feature vectors or signature. This is called offline feature extraction. The main advantage of using CBIR system is that the system uses image features instead of using the image itself. So, CBIR is cheap, fast, and efficient over image search methods[2]. A key component of the CBIR system is feature extraction. A feature is a characteristic that can capture a certain visual property of the image. One of the key issues with any kind of image processing is the need to extract useful information from the raw data (such as recognizing the presence of particular shapes or textures) before any kind of reasoning about the image’s contents is possible[2]. All CBIR systems view the query image and the target images as a collection of features. These features, or image signatures, characterize the content of the image. The advantages of using image features instead of the original image pixels appear in image representation and comparison for retrieving. When the image features are used for matching, it almost do compression for the image and use the most important content of the image. This also bridges the gaps between the semantic meaning of the image and the pixel representation[2].Early studies on CBIR used a single visual feature content such as color, texture, or shape to describe the image. The drawback of this method is that using one feature is not enough to describe the image since the image contains various visual feature characteristics[2]. This paper propose to extract color , texture and edge direction features from the image.