Pi Ming Hong, Tong C S, Choy Siu Kai, Zhang Hong
Department of Computing Science, University of Alberta, Edmonton, Canada.
IEEE Trans Image Process. 2006 Oct;15(10):3078-88. doi: 10.1109/tip.2006.877509.
This paper presents a novel, effective, and efficient characterization of wavelet subbands by bit-plane extractions. Each bit plane is associated with a probability that represents the frequency of 1-bit occurrence, and the concatenation of all the bit-plane probabilities forms our new image signature. Such a signature can be extracted directly from the code-block code-stream, rather than from the de-quantized wavelet coefficients, making our method particularly adaptable for image retrieval in the compression domain such as JPEG2000 format images. Our signatures have smaller storage requirement and lower computational complexity, and yet, experimental results on texture image retrieval show that our proposed signatures are much more cost effective to current state-of-the-art methods including the generalized Gaussian density signatures and histogram signatures.
