Data Engineering

As network connectivity has continued its explosive growth and as storage devices have become smaller, faster, and less expensive, the number of online digitized images has increased rapidly. Successful queries on large, heterogeneous image collections cannot rely on the use of text matching alone. In this paper we describe how we use image analysis in conjunction with an object relational database to provide both textual and content-based queries on a very large collection of digital images. We discuss the effects of feature computation, retrieval speed, and development issues on our feature storage strategy. 1 Introduction A recent search of the World Wide Web found 16 million pages containing the word "gif " and 3.2 million containing "jpeg" or "jpg." Many of these images have little or no associated text, and what text they do have is completely unstructured. Similarly, commercial image databases may contain hundreds of thousands of images with little useful text. To fully utilize..