Supporting Content-Based Retrieval in Large Image Database Systems

Abstract

In this paper, we investigate approaches to supporting effective and efficient retrieval of image data based on content. We first introduce an effective block-oriented image decomposition structure which can be used to represent image content in image database systems. We then discuss the application of this image data model to content-based image retrieval. Using wavelet transforms to extract image features, significant content features can be extracted from image data through decorrelating the data in their pixel format into frequency domain. Feature vectors of images can then be constructed. Content-based image retrieval is performed by comparing the feature vectors of the query image and the decomposed segments in database images. Our experimental analysis illustrates that the proposed block-oriented image representation offers a novel decomposition structure to be used to facilitate effective and efficient image retrieval.