What is Content-Based Image Retrieval

Images are generated everywhere from various sources. It could be satellite pictures, biomedical, scientific, entertainment, sports and many more, generated through video camera, ordinary camera, x-ray machine, and so on. These images are stored in image databases. Content-based image retrieval (CBIR) technique is being applied to access these vast volumes of images from databases efficiently. Some of the areas, where CBIR is applied, include weather forecasting, scientific database management, art galleries, law enforcement, and fashion design. Initially image representation was based on various attributes of the image like height, length, angle and was accessed using those attributes extracted manually and managed within the framework of conventional database management systems. Queries are specified using these attributes. This entails a high-level of image abstraction (Chen, Li & Wang, 2004). Also there was feature-based object-recognition approach where the process was automated to extract images based on color, shape, texture, and spatial relations among various objects of the image. Recently combining these two approaches, efficient image representation and query-processing algorithms, have been developed to access image databases. Recent CBIR research tries to combine both of these above mentioned approach and has given rise to efficient image representations and data models, query-processing algorithms, intelligent query interfaces and domain-independent system architecture. As we mentioned, image retrieval can be based on lowlevel visual features such as color (Antani, Rodney Long & Thoma, 2004; Deb & Kulkarni, 2007; Deb & Kulkarni, 2007a; Ritter & Cooper, 2007; Srisuk & Kurutach, 2002; Sural, Qian & Pramanik, 2002; Traina, Traina, Jr., Bueno, & Chino, 2003; Verma & Kulkarni, 2004), texture (Antani et al., 2004; Deb & Kulkarni, 2007a; Zhou, Feng & Shi, 2001), shape (Ritter & Cooper, 2007; Safar, Shahabi & Sun, 2000; Shahabi & Safar, 1999; Tao & Grosky, 1999), high-level semantics (Forsyth et al., 1996), or both (Zhao & Grosky, 2001). But most of the works done so far are based on the analysis of explicit meanings of images. But image has implicit meanings as well, which give more and different meanings than only explicit analysis provides. In this paper we provide the concepts of emergence index and analysis of the implicit meanings of the image which we believe should be taken into account in analysis of images of image or multimedia databases.