In this paper, we propose a new hybrid Local Binary Pattern (LBP) based on Hessian matrix and Attractive Center-Symmetric LBP (ACS-LBP), called Hess-ACS-LBP. The Hessian matrix provides the directional derivative information of different texture regions, while ACS-LBP reveals the local texture features efficiently. To obtain the macro- and micro-structure textural changes, Hessian matrix is calculated in a multiscale schema. Multiscale Hessian matrix presents the intrinsic local geometry of the texture changes. The magnitude information of the Hessian matrix is used in the ACS-LBP method. A cross-scale joint coding strategy is used to construct Hess-ACS-LBP descriptor. Finally, histogram concatenation is carried out. Extensive experiments on eight texture databases of CUReT, USPTex, KTH-TIPS2b, MondialMarmi, OuTeX TC_00013, XU HR, ALOT and STex validate the efficiency of the proposed method. The proposed Hess-ACS-LBP method achieves about 20% improvement over the original LBP method and 1%–11% improvement over the other state-of-the-art hand-crafted LBP methods in terms of classification accuracy. Besides, the experimental results show that the proposed method achieves up to 32% better results than the state-of-the-art deep learning based methods. Especially, the performance of the proposed method on ALOT and STex datasets containing many classes is remarkable.

A novel active learning‐based electrocardiogram (ECG) signal classification method using eigenvalues and deep learning is proposed. Six statistical features relating to ECG beat intervals are calculated separately for each heartbeat. Both statistical features and eigenvalues of ECG beats are combined into a single feature vector. The eigenvalues of ECG beats are used as an input to denoising autoencoder (DAE). Weighted ECG beat intervals are calculated by using ten‐fold cross‐validation approach. To learn an efficient feature representation from the hybrid feature vector, DAE is used in an unsupervised way. After completing the feature learning procedure, a softmax regression layer is added on the top of the resulting hidden layer of DAE, and thus a suitable deep neural network (DNN) architecture is built. The learned features obtained from the autoencoder layers are fed to the softmax regression layer for classification. To update weights of the proposed eigenvalues‐based DNN model, ECG beats are labelled by the medical expert are used. In order to determine the most informative beats, entropy and Breaking‐Ties are also used as selection criteria. The proposed method is evaluated in terms of ECG beats classes. The classification performance of the authors’ proposed model is also compared with the several conventional machine learning classifiers.

To enhance the weakness of Local Binary Pattern (LBP) and its state-of-the-art variants, this paper presents a new variant of the local concave microstructure pattern (LCvMSP). The proposed multi-scale shape index based texture descriptor is named as SI-LCvMSP. Contrarily to the original LBP and LCvMSP, SI-LCvMSP uses the shape index instead of the original texture image in the kernel function. The shape index is a differential calculation and it can be calculated from local second-order derivatives of texture images. It captures microstructure and macrostructure texture information mathematically. As textural features, we use multi-scale and multi-resolution shape index information as well as rotation-invariant uniform LBP. Thus, we obtain the discriminative feature representation schema to construct cross-scale joint coding. The proposed method has a high discriminability and is less sensitive to image transforms such as rotation and illumination. Experimental results show that the SI-LCvMSP descriptor can improve classification accuracy.