Image Retrieval using 2D Dual-Tree Discrete Wavelet Transform

The large amount of image collections available from a variety of sources has posed increasing technical challenges to computer systems to store/transmit and index/manage the image data to make such collections easily accessible. Here to search and retrieve the expected images from the database we need Content Based Image Retrieval system. This paper proposes a new feature vector based on 2D Dual-tree Discrete Wavelet Transform. One of the advantages of the dual-tree complex wavelet transform is that it can be used to implement 2D wavelet transforms that are more selective with respect to orientation than is the separable 2D DWT. Most of the natural images have short span high frequencies and low frequencies extending for larger span. Hence, the design of our feature vector is such that it provides higher spatial localization and lower frequency resolution at higher frequencies and the reverse for lower frequencies. The energy and mean of the frequency content of the image at various sub bands and different spatial resolution (higher for higher frequency bands) is stored as feature vector. Thus, the given feature vector encodes high frequency information as well. General Terms Content Based image retrieval aims at developing new effective techniques to search and browse similar images from the large image database by analyzing the image contents. With the rapid development of technology of multimedia, the traditional information retrieval techniques based on keywords are not sufficient, content based image retrieval (CBIR) has been an active research topic.