Gangyao Kuang

Extended local binary patterns for texture classification

This paper presents a novel approach for texture classification, generalizing the well-known local binary pattern (LBP) approach. In the proposed approach, two different and complementary types of features (pixel intensities and differences) are extracted from local patches. The intensity-based features consider the intensity of the central pixel (CI) and those of its neighbors (NI); while for the difference-based feature, two components are computed: the radial-difference (RD) and the angular-difference (AD). Inspired by the LBP approach, two intensity-based descriptors CI-LBP and NI-LBP, and two difference-based descriptors RD-LBP and AD-LBP are developed. All four descriptors are in the same form as conventional LBP codes, so they can be readily combined to form joint histograms to represent textured images. The proposed approach is computationally very simple: it is totally training-free, there is no need to learn a texton dictionary, and no tuning of parameters. We have conducted extensive experiments on three challenging texture databases (Outex, CUReT and KTHTIPS2b). Outex results show significant improvements over the classical LBP approach, which clearly demonstrates the great power of the joint distributions of these proposed descriptors for gray-scale and rotation invariant texture classification. The proposed method produces the best classification results on KTHTIPS2b, and results comparable to the state-of-the-art on CUReT.

An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images

An adaptive and fast constant false alarm rate (CFAR) algorithm based on automatic censoring (AC) is proposed for target detection in high-resolution synthetic aperture radar (SAR) images. First, an adaptive global threshold is selected to obtain an index matrix which labels whether each pixel of the image is a potential target pixel or not. Second, by using the index matrix, the clutter environment can be determined adaptively to prescreen the clutter pixels in the sliding window used for detecting. The G 0 distribution, which can model multilook SAR images within an extensive range of degree of homogeneity, is adopted as the statistical model of clutter in this paper. With the introduction of AC, the proposed algorithm gains good CFAR detection performance for homogeneous regions, clutter edge, and multitarget situations. Meanwhile, the corresponding fast algorithm greatly reduces the computational load. Finally, target clustering is implemented to obtain more accurate target regions. According to the theoretical performance analysis and the experiment results of typical real SAR images, the proposed algorithm is shown to be of good performance and strong practicability.

Sorted random projections for robust rotation-invariant texture classification

This paper presents a simple, novel, yet very powerful approach for robust rotation-invariant texture classification based on random projection. The proposed sorted random projection maintains the strengths of random projection, in being computationally efficient and low-dimensional, with the addition of a straightforward sorting step to introduce rotation invariance. At the feature extraction stage, a small set of random measurements is extracted from sorted pixels or sorted pixel differences in local image patches. The rotation invariant random features are embedded into a bag-of-words model to perform texture classification, allowing us to achieve global rotation invariance. The proposed unconventional and novel random features are very robust, yet by leveraging the sparse nature of texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We report extensive experiments comparing the proposed method to six state-of-the-art methods, RP, Patch, LBP, WMFS and the methods of Lazebnik et al. and Zhang et al., in texture classification on five databases: CUReT, Brodatz, UIUC, UMD and KTH-TIPS. Our approach leads to significant improvements in classification accuracy, producing consistently good results on each database, including what we believe to be the best reported results for Brodatz, UMD and KTH-TIPS.

Sorted Random Projections for robust texture classification

This paper presents a simple and highly effective system for robust texture classification, based on (1) random local features, (2) a simple global Bag-of-Words (BoW) representation, and (3) Support Vector Machines (SVMs) based classification. The key contribution in this work is to apply a sorting strategy to a universal yet information-preserving random projection (RP) technique, then comparing two different texture image representations (histograms and signatures) with various kernels in the SVMs. We have tested our texture classification system on six popular and challenging texture databases for exemplar based texture classification, comparing with 12 recent state-of-the-art methods. Experimental results show that our texture classification system yields the best classification rates of which we are aware of 99.37% for CUReT, 97.16% for Brodatz, 99.30% for UMD and 99.29% for KTH-TIPS. Moreover, combining random features significantly outperforms the state-of-the-art descriptors in material categorization.

Sparse Representation of Monogenic Signal: With Application to Target Recognition in SAR Images

In this letter, the classification via sparse representation of the monogenic signal is presented for target recognition in SAR images. To characterize SAR images, which have broad spectral information yet spatial localization, the monogenic signal is performed. Then an augmented monogenic feature vector is generated via uniform down-sampling, normalization and concatenation of the monogenic components. The resulting feature vector is fed into a recently developed framework, i.e., sparse representation based classification (SRC). Specifically, the feature vectors of the training samples are utilized as the basis vectors to code the feature vector of the test sample as a sparse linear combination of them. The representation is obtained via l1-norm minimization, and the inference is reached according to the characteristics of the representation on reconstruction. Extensive experiments on MSTAR database demonstrate that the proposed method is robust towards noise corruption, as well as configuration and depression variations.

SAR Target Recognition via Joint Sparse Representation of Monogenic Signal

In this paper, the classification via sprepresentation and multitask learning is presented for target recognition in SAR image. To capture the characteristics of SAR image, a multidimensional generalization of the analytic signal, namely the monogenic signal, is employed. The original signal can be then orthogonally decomposed into three components: 1) local amplitude; 2) local phase; and 3) local orientation. Since the components represent the different kinds of information, it is beneficial by jointly considering them in a unifying framework. However, these components are infeasible to be directly utilized due to the high dimension and redundancy. To solve the problem, an intuitive idea is to define an augmented feature vector by concatenating the components. This strategy usually produces some information loss. To cover the shortage, this paper considers three components into different learning tasks, in which some common information can be shared. Specifically, the component-specific feature descriptor for each monogenic component is produced first. Inspired by the recent success of multitask learning, the resulting features are then fed into a joint sparse representation model to exploit the intercorrelation among multiple tasks. The inference is reached in terms of the total reconstruction error accumulated from all tasks. The novelty of this paper includes 1) the development of three component-specific feature descriptors; 2) the introduction of multitask learning into sparse representation model; 3) the numerical implementation of proposed method; and 4) extensive comparative experimental studies on MSTAR SAR dataset, including target recognition under standard operating conditions, as well as extended operating conditions, and the capability of outliers rejection.

Target Recognition via Information Aggregation Through Dempster–Shafer's Evidence Theory

In this letter, a novel classification via information aggregation through Dempster-Shafers (DS) evidence theory has been presented to target recognition in a SAR image. Although the DS theory of evidence has been widely studied over the decades, less attention has been paid to its application for target recognition. To capture the characteristics of a SAR image, this letter exploits a new multidimensional analytic signal named monogenic signal. Since the components of the monogenic signal are of a high dimension, it is unrealistic to be directly used. To solve the problem, an intuitive idea is to derive a single feature by these components. However, this strategy usually results in some information loss. To boost the performance, this letter presents a classification framework via information aggregation. The monogenic components are individually fed into a recently developed algorithm, i.e., sparse representation-based classification, from which the residual with respect to each target class can be produced. Since the residual from a query sample reflects the distance to the manifold formed by the training samples of a certain class, it is reasonable to be used to define the probability mass. Then, the information provided by the monogenic signal can be aggregated via Dempsters rule; hence, the inference can be reached.

A Threshold Selection Method Using Two SAR Change Detection Measures Based on the Markov Random Field Model

This letter presents a threshold selection method in change detection (CD) with synthetic aperture radar (SAR) images, which combines the characteristics of two different CD measures by using the Markov random field model. One is the well-known log-ratio CD measure, and the other is derived from the likelihood ratio and is based on the statistical properties of SAR intensity images. The proposed unsupervised CD algorithm overcomes the shortcomings and strengthens the advantages of these two measures. The experimental results with two pairs of SAR images show that the proposed algorithm is effective and better than the algorithms using the two aforementioned CD measures.

Target Recognition in SAR Images via Classification on Riemannian Manifolds

In this letter, synthetic aperture radar (SAR) target recognition via classification on Riemannian geometry is presented. To characterize SAR images, which have broad spectral information yet spatial localization, a 2-D analytic signal, i.e., the monogenic signal, is used. Then, the monogenic components are combined by computing a covariance matrix whose entries are the correlation of the components. Since the covariance matrix, a symmetric positive definite one, lies on the Riemannian manifold, it is unreasonable to be dealt with by the standard learning techniques. To address the problem, two classification schemes are proposed. The first maps the covariance matrix into the vector space and feeds the resulting descriptor into a recently developed framework, i.e., sparse representation-based classification. The other embeds the Riemannian manifold into an implicit reproducing kernel Hilbert space, followed by least square fitting technique to recover the test. The inference is reached by evaluating which class of samples could reconstruct the test as accurately as possible.

Generalized Local Binary Patterns for Texture Classification

This paper presents a novel approach for texture classification, generalizing the wellknown local binary patterns (LBP). In the proposed approach, two different and complementary types of features are extracted from local patches, based on pixel intensities and differences. Inspired by the LBP approach, two intensity-based and two difference-based descriptors are developed. All four descriptors have the same form as the conventional LBP codes, thus they can be readily combined to form joint histograms to represent textured images. The proposed approach is computationally simple and is training-free: there is no need to learn a texton dictionary and no tuning of parameters. Extensive experimental results on two challenging texture databases (Outex and KTHTIPS2b) show that the proposed approach significantly outperforms the classical LBP approach and other state-of-the-art methods with a nearest neighbor classifier.