Fast Image(Infomatoin) Retrieval (IR) Using Wavelet Coding

Image Retrieval (IR) is one of the most exciting and fastest growing research areas in the field of infomation technology. Large and distributed collections of scientific, artistic, and commercial data comprising images, text, audio and video abound in our information-based society [1]. There must be an effective and precise method for users to search, browse, and interact with these large collections and do so in a timely manner. A common ground in most of current IR systems is to exploit low-level features such as color, texture and shape, which can be extracted by a machine automatically. While semantic level retrieval would be more desirable for users, given the current state of technology in image understanding, this is still very difficult to achieve. This is especially true when one has to deal with a heterogeneous and unpredictable image collection such as from the Internet. Most current feature extraction techniques suffer from the problem that they do not retain any spatial information. Some more recent systems exploited wavelet basis coefficient to cope with this problem. Wavelets provide a powerful and efficient mathematical tool to process images at multiple scales. Wavelets can decomposite fine(high dimensional) images into coarse resolutions. That is, the wavelets scale(downsample) large image into smaller one. Then traditional methods, such as histogram feature extractions, are used to do image retrieval with these coarse images. This improves the image retrieval speed. In addition, wavelet decomposition provides a very good approximation of images and th