Content Based Image Retrieval: Survey

The requirement for development of CBIR is enhanced due to tremendous growth in volume of images as well as the widespread application in multiple fields. Texture, color, shape and spatial layout are the underlying traits to represent and index the images. These peculiar features of images are extracted and implemented for a similarity check among images. The problem of content based image retrieval is based on generation of peculiar query. For relevant images that meet their information need, an automated search is initiated by drawing a sketch or with the submission of image having similar features. Similarity between extracted features can be measured by using different algorithms. The use of relevance feedback as a post retrieval step enhances the optimization of the process. The necessity to explore the ever growing volume of image and video is motivating the development of efficient CBIR algorithms. Different algorithms and models for the retrieval of images have been explored over the last twenty years. In this paper an analysis of visual contents of image is done with respect to features related to low level after extracting from image that are color, texture and shape. Here most popular algorithms of feature extraction and relevance feedback that try to bridge extracted low level features and features with high level semantics gap from image are discussed. A brief overview of an algorithm has been presented which is based on fuzzy logic and is used for the selection of peculiar features.