title: A fast and effective model for wavelet subband histograms and its application in texture image retrieval.
abstract: 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.
authors.affiliation: Department of Computing Science, University of Alberta, Edmonton, Canada. minghong@cs.ualberta.ca
authors.forename: Ming Hong
authors.initials: MH
authors.lastname: Pi
authors.forename: C S
authors.initials: CS
authors.lastname: Tong
authors.forename: Siu Kai
authors.initials: SK
authors.lastname: Choy
authors.forename: Hong
authors.initials: H
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted