Jeffrey R. Bach

Virage video engine

The temporal and multi-modal nature of video increases the dimensionality of content based retrieval problem. This places new demands on the indexing and retrieval tools required. The Virage Video Engine (VVE) with the default set of primitives provide the necessary frame work and basic tools for video content based retrieval. The video engine is a flexible platform independent architecture which provides support for processing multiple synchronized data streams like image sequences, audio and closed captions. The architecture allows for multi-modal indexing and retrieval of video through the use of media specific primitives. This paper presents the use of the VVE framework for content based video retrieval.

A visual information management system for the interactive retrieval of faces

The complex nature of two-dimensional image data has presented problems for traditional information systems designed strictly for alphanumeric data. Systems aimed at effectively managing image data have generally approached the problem from two different views: They either possess a strong database component with little image understanding, or they serve as an image repository for computer vision applications, with little emphasis on the image retrieval process. A general architecture for visual information-management systems (VIMS), which combine the strengths of both approaches, is presented. The system utilizes computer vision routines for both insertion and retrieval and allows easy query-by-example specifications. The vision routines are used to segment and evaluate objects based on domain-knowledge describing the objects and their attributes. The vision system can then assign feature values to be used for similarity-measures and image retrieval. A VIMS developed for face-image retrieval is presented to demonstrate these ideas. >

An Interactive Image Management System for Face Information Retrieval

The complex nature of two-dimensional image data has presented problems for traditional information systems designed strictly fo alpha-numeric data. In many systems, images are simply treated as an additional data type, making no use of the actual image data. However, this image data provides an enormous amount of information and should be utilized for more powerful retrieval capabilities. In this paper we present a general system architecture for image management systems which combines the strengths of computer vision systems with traditional information management techniques. This system will provide a general framework for the development of any domain-specific application. Our approach utilizes computer vision routines for both insertion and retrieval, and allows easy query-by-example specifications. The vision routines are used to segment and evaluate images based on descriptions of the domain objects and their attributes. The vision system can then assign feature values to be used for similarity-measures and image retrieval. An image management system developed for face-image retrieval is presented to demonstrate these ideas.