Texture Classification Using Refined Histogram

Abstract

In this correspondence, we propose a novel, efficient, and effective Refined Histogram (RH) for modeling the wavelet subband detail coefficients and present a new image signature based on the RH model for supervised texture classification. Our RH makes use of a step function with exponentially increasing intervals to model the histogram of detail coefficients, and the concatenation of the RH model parameters for all wavelet subbands forms the so-called RH signature. To justify the usefulness of the RH signature, we discuss and investigate some of its statistical properties. These properties would clarify the sufficiency of the signature to characterize the wavelet subband information. In addition, we shall also present an efficient RH signature extraction algorithm based on the coefficient-counting technique, which helps to speed up the overall classification system performance. We apply the RH signature to texture classification using the well-known databases. Experimental results show that our proposed RH signature in conjunction with the use of symmetrized Kullback-Leibler divergence gives a satisfactory classification performance compared with the current state-of-the-art methods.

Efficient Texture Image Retrieval Using Copulas in a Bayesian Framework

“…It is further worth noting why we omitted comparative tests against some of the approaches mentioned in Section I, such as the Refined Histogram technique [17] or a Hidden Markov Tree based approach. First, the Refined Histogram technique is particularly designed to enhance runtime behavior of the GG based margin-only approach of Do & Vetterli [5].…”

“…A considerable amount of research work has been devoted to the statistical modeling of DCT or DWT subband coefficients, primarily focusing on the Generalized Gaussian (GG) distribution [5], [14], [15]. Recently, the Generalized Gamma (GGamma) distribution [16] and the Refined Histogram [17] have been proposed as reasonable alternatives. In particular, the Refined Histogram approach is based on the idea of modeling DWT coefficients by Product Benoulli distributions [18], [19] which can be estimated very efficiently.…”

Intelligent reorganized discrete cosine transform for reduced reference image quality assessment

“…Nevertheless, the WNISM has some limitations, which are investigated in detail by Ma et al [13] and are summarized as: (a) the coefficient connectivity is not taken into account in the wavelet subbands [10]; (b) it performs well on a single distortion type but is not effective for multiple distortions when analyzed together [14]; (c) in the case of KLD, it is computationally expensive when two different GGDs are computed [15], which indeed is not feasible for practical applications. In addition, since KLD is asymmetric [16], the distance between the distorted and original images is not the same.…”

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).…”