International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 12 | Dec-2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 828 A Survey on Content Based Image Retrieval for Reducing Semantic Gap T.RajaSenbagam 1 , Dr.R.Shanmugalakshmi 2 1 Assistant Professor, Department of CSE & Government College of Technology, Coimbatore-13 2 Professor & Head, Department of EEE & Government College of Engineering, Salem Tamil Nadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Image retrieval is the process of searching and retrieving images from large image database by usinga query image. Now there is an increasing volume of digital images available in the World Wide Web produced by scientific, educational, medical, industrial, and other applications are easily available to users. But it is a challenging task for the user to retrieve an image from this huge volume of image database by manual annotation method or otherwise calledas text based retrieval method. Hence we move on to a newimage retrieval method called content based image retrieval (CBIR).By combining the low level features and high level semantic feature an image can be retrieved in CBIR. Content based image retrieval uses the visual content of the image to retrieve the image from large image database. But reducing the semantic gap between the low level visual features and the high level image semantics is a challenging task in content based image retrieval. Here in this paper we have provided a comparative study of various techniques of CBIR and the various techniques to reduce the semantic gap between the query image and the retrieved image. Key Words: Content Based Image Retrieval, Semantic Gap, Low level feature, High level feature, Relevance Feedback, Text Based Image retrieval 1. INTRODUCTION Content Based Image Retrieval (CBIR) is a searching technique that can be used for retrieving most similar images from the large collection of database and it is also called as Query By Image Content (QBIC) [1]. The term content refers to colors, shapes, textures or any other information that can be derived from the image itself. Hence the recognition and retrieval of information in CBIR is based on content of images only instead of metadata such as keywords, tags or descriptions associated with the images [2]. Types of Image Retrieval Image retrieval is of two categories: 1. Text-Based Image Retrieval 2. Content-Based Image Retrieval Text-Based Image Retrieval Text-based image retrieval is a traditional technique to retrieve an image from database. It uses the keyword annotation to retrieve the image. It has several advantages such as ease of retrieval, adaptable for small databases and keyword usage for representing more images. But it is still time consuming, incomplete and not standardized. Content-Based Image Retrieval CBIR uses the visual contents of the image to retrieve digital image from the large set of databases. The visual contents of an image are the low level features such as color, shape, texture and spatial layout. The main advantage of CBIR is having more accuracy when compares with text based image retrieval and reduction of dissimilarity between the semantic content of images. But the image retrieval procedure is complex and slower when compared to text based system. Fig:1.1 CBIR System The Overview of CBIR system is shown in fig. 1.1. Consider this CBIR system, in which the images are captured by the digital camera and the common features are extracted from the captured image which will be stored in the image database. The features are extracted from various images in the image database by applying feature extraction algorithms and the feature vector values of different images are stored in the feature vector Database. In order to retrieve a particular image from this huge vector database a query image is given as input to the system , the system will then extract the features such as color, texture and shape from the given query image and compute the feature vector value of the query image. The system will match the feature vector of query image with the image in the database and retrieve the