Large-scale indexing and retrieval system for local image features

Abstract

A new indexing system for multiresolution image retrieval has been developed. The system analyzes not only whole images but also--for the first time--local features present in the images. This capability is important in managing large-scale image systems such as video databases and medical image databases. Key to the method is the use of an innovative wavelet, called the W-transform. This transform is characterized by its ability to handle signals of arbitrary size. Equally important, the W-transform is not constrained by assumptions about periodicity. That is, the transformed signal in some location is related only to the signals at its neighboring location. These properties make the W-transform a natural tool for analyzing local image features. A brief discussion is given of how the system works. The feature-oriented image indexing and retrieval method offers two major advantages over traditional multiresolution histogram comparison methods. The response time for retrieving image coarse components is considerably faster than that for retrieving whole images--thereby dramatically speeding image retrieval over the Ethernet. Moreover, the new method can be used to search images that are significantly different from the query image as a whole but contain local features identical with or similar to those in the query image.