Abstract

This paper describes WBIIS (Wavelet-Based Image Indexing and Searching), a new image indexing and retrieval algorithm with partial sketch image searching capability for large image databases. The algorithm characterizes the color variations over the spatial extent of the image in a manner that provides semantically-meaningful image comparisons. The indexing algorithm applies a Daubechies ' wavelet transform for each of the three opponent color components. The wavelet coe cients in the lowest few frequency bands, and their variances, are stored asfeature vectors. To speed up retrieval, a two-step procedure is used that rst does a crude selection based on the variances, and then renes the search by performing a feature vector match between the selected images and the query. For better accuracy in searching, two level multiresolution matching may also be used. Masks are used for partialsketch queries. This technique performs much better in capturing coherence of image, object granularity, local color/texture, and bias avoidance than traditional color layout algorithms. When tested on a database of more than 10,000 general-purpose images, WBIIS is much faster and more accurate than traditional algorithms.

Citations