A Fast and Effective Model for Wavelet Subband Histograms and Its Application in Texture Image Retrieval

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 signatu...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 2006-10, Vol.15 (10), p.3078-3088
Main Authors: Ming Hong Pi, Tong, C.S., Siu Kai Choy, Hong Zhang
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Bit-plane probabilities
Block codes
Coding, codes
Computational complexity
Computer Graphics
Density
Discrete wavelet transforms
embedded block coding with optimized truncation (EBCOT)
Exact sciences and technology
Histograms
Image coding
Image databases
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image retrieval
Image storage
Information Storage and Retrieval - methods
Information, signal and communications theory
JPEG2000
Mathematical models
Miscellaneous
Pattern Recognition, Automated - methods
Retrieval
Signal and communications theory
Signal processing
Signal Processing, Computer-Assisted
Signal representation. Spectral analysis
Signal, noise
Signatures
Surface layer
Telecommunications and information theory
Texture
textures
Transform coding
Wavelet
Wavelet coefficients
wavelet signatures
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2006.877509