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.

A performance evaluation of gradient field HOG descriptor for sketch based image retrieval

“…These systems were based primarily on global color histograms [7], spatial patterns [19] or region adjacency [20,21]. Robust color blob-based matching was later proposed using spectral descriptors, such as wavelets [22,23] which have more recently proven useful for more general image retrieval using colour texture [24,25].…”

Watermark detection on quantized transform coefficients using product bernoulli distributions

“…In contrast to the Gaussian distribution (which arises as a special case of the GGD for c = 2), the GGD is a leptokurtic distribution which allows heavy-tails. The distribution of the transform coefficients is symmetric around zero [11], hence it can be characterized by the histogram of absolute values of the subband coefficients [16]. In case the coefficients have been quantized to integer values, an absolute coefficient |x| of a particular signal can be represented by…”

“…Assuming statistical independence and denoting the model parameters by pi = P (Xi = 1), the joint distribution of Eq. (3) can be written as a product of Bernoulli distributions (PBD) [16,4] …”

“…In this work we propose we novel watermark detector derived from on a simple model for quantized (integer) DWT or DCT coefficient values based on the bit-plane probability signatures recently introduced for texture retrieval applications [16,4]. The advantages of the proposed watermark detector include the reliable estimation of the model parameters even on heavily quantized data, straightforward integration of the method in multimedia codecs as the computation of the detection statistic can be implemented using integer arithmetic only, thus permitting efficient implementation.…”

Texture Classification by Bit-plane Multifractal Spectrum and Bit-plane Barycentric Coordinates of Wavelet Coefficients Based on SVD

“…Recently, models based on wavelet subband coefficients have also been used on texture classification. The existing models in literatures contain the Characteristic Generalized Gaussian Density (CGGD) model [12], the Bit-plane Probability (BP) model [13,14], the Refined Histogram [15], the Local Energy Histogram [16], and so on. Particularly, the Bit-plane Probability (BP) signature is a very competitive feature by modeling wavelet high-frequency subband coefficients via the Product Bernoulli Distributions (PBD).…”