Image Retrieval Using Semantics of Query Image

Computer cognition technology may impose the need to have a system to organize abundantly available digital images for easy categorization and retrieval. Certainly, the revolutionary internet and digital technologies have already imposed such need. Further, the need to have versatile and general purpose image retrieval (IR) system for a very large image database has attracted focus of many researchers of information technology-giants and leading academic institutions for development of IR techniques. These techniques encompass diversified areas, viz. image segmentation, image feature extraction, representation, mapping of features to semantics, storage and indexing, image similarity-distance measurement and retrieval - making IR system development a challenging task. Visual information retrieval requires a large variety of knowledge. The clues that must be pieced together when retrieving images from a database include not only elements such as color, texture and shape but also the relation of the image contents to alphanumeric information, and the higher-level concept of the meaning of objects in the scene. In most of the existing image retrieval systems, query response time depends on the database size. An increase in the database size linearly increases the query response time, or to keep the query time constant quality of the result is compromise.