Modeling and retrieving images by content

Abstract

A content-based image retrieval system (CBIR) is required to effectively utilize information from image databases. Content-based retrieval is characterized by the ability of the system to retrieve relevant images based on their visual and semantic contents rather than by using atomic attributes or keywords assigned to them. In this paper, we provide a taxonomy for approaches to image retrieval and describe their characteristics and limitations. We examined a number of image database applications to discover their retrieval requirements and to structure the requirements from a domain-independent perspective. This study enabled us to provide a taxonomy for image attributes and to propose a number of generic query operators. These operators are adequate to realize CBIR in a number of diverse applications. We propose a novel system architecture for CBIR that supports the generic query operators. The architecture is structured in a way to enable applications to inherit only those query operators that are useful in the domain. We have developed a partial prototype implementation of this architecture. The versatility and effectiveness of the architecture is demonstrated by configuring the prototype implementation for two image retrieval applications: realtors information system and face retrieval system. The first application is for real estate marketing and the other is for law enforcement and criminal investigation.