Abstract The definition of an image's category from a database with huge texture categories needs massive computation and time cost. Existing texture classification works focus on texture representation to improve the accuracy and efficiency of classification. This research wants to reduce the categories of the main classifier to decrease the comparison time of classification. To overcome computation time, a features' value range (FR) approach to enhance the throughput of texture classification is proposed. The proposed approach decreases the number of candidate categories as a pre‐classifier in a two‐step serial classification. With the decrease in the number of candidates, the main classifier can work on a few categories to find the final category. Here, configuration parameters are defined and some criteria are proposed for evaluating the FR approach. The performance of the FR is evaluated in the presence of different levels of Gaussian noise. Finally, it is shown that using effective features (EF) and hardware implementation approaches can extend the applicability of the FR approach. Experimental results depicted that the throughput of the final decision increased up to 14.85× with considerable reliability.

Abstract Automatic analysis, understanding typical activities, and identifying vehicle behaviour in crowded traffic scenes are fundamental and challenging tasks for traffic video surveillance. Some recent researches have been using machine learning approaches to extract meaningful patterns occurring in a traffic scene, for example, intersection. In this regard, we convert visual patterns and features to visual words using dense and sparse optical flow and learning traffic motion patterns with group sparse topical coding (GSTC) algorithm. In the first step of the proposed algorithm, the input traffic video is divided into non‐overlapping clips. After that, motion vectors are extracted using dual TV‐L1 as a dense optical flow and Lucas–Kanade as a sparse optical flow and converted to flow words. For learning traffic motion patterns, the GSTC algorithm, that is, a non‐probabilistic topic model (TM) has been applied. These patterns represent priors on observable motion, which can be utilised to describe a scene and answer behaviour questions such as what are the motion patterns in a traffic scene and what is going on. The experimental results which have been obtained using a real dataset, QUML, show that the combination of the GSTC + dual TV‐L1 extracts more traffic motion patterns in comparison with the GSTC + Lucas–Kanade and previous studies.

Abstract Automatic facial expression recognition, which has many applications such as drivers, patients, and criminals' emotions recognition, is a challenging task. This is due to the variety of individuals and facial expression variability in different conditions, for instance, gender, race, colour and changing illumination. In addition, there are many regions in a face image such as forehead, mouth, eyes, eyebrows, nose, cheeks and chin, and extracting features of all these regions are expensive in terms of computational time. Each of the six basic emotions of anger, disgust, fear, happiness, sadness and surprise affect some regions more than the other regions. The goal of this study is to evaluate the performance of enhanced local binary pattern, pyramid histogram of oriented gradients feature‐extraction algorithms and their combination in terms of recognition accuracy, feature vector length and computational time on one, two and three combined regions of a face image. Our experimental results show that the combination of both feature‐extraction algorithms yields an average recognition accuracy of 95.33% using three regions, that is, the mouth, nose and eyes on Cohn–Kanade dataset. Besides, the mouth region is the most important part in terms of accuracy in comparison to eyes, nose and combination of both eyes and nose regions.

Abstract State estimation and dynamical model identification from observed data has been an attractive research area with a wide range of applications such as communication, navigation, radar target tracking, and system control. A method of Adaptive Fuzzy Unscented Kalman/H∞ Filter (AFUKH∞) to estimate non‐linear systems is presented using a combination of the Unscented Kalman Filter (UKF) and Unscented H∞ Filter (UH∞F). The proposed filter does not need linearisation and is based on a combination of gain, a priori state estimation, and a priori measurement estimation in each time step. The performance of the filter is adaptively adjustable. Thus, its efficiency is better than the other two filters. Two fuzzy logic systems are proposed that determine the weight of the UKF and UH∞F filters at each step. These two fuzzy systems are designed to be independent of the dynamics of the system (problem). The proposed filter is referred to as a hybrid AFUKH∞‐II. In the proposed method, the state of the feedback is used as input that improves the efficiency of the filter. The challenge of reentry vehicle tracking and the state estimation of a magnetic motor as two non‐linear high‐order problems are used as benchmarks, and the results are compared with the UKF, UH∞F, and AFUKH∞ filters. The experiments show that an estimation of the proposed hybrid filter (AFUKH∞‐II) is improved against state‐of‐the‐art filters. Also, estimation error and variance values of the proposed filter in the presence of Gaussian noise is decreased by 270% and 370%, respectively, compared with the AFUKH∞ filter.

Local binary patterns (LBPs) are a well‐known operator that shows the ability for rotation and scale invariant texture classification. A recent extension of this operator is the pyramid transform domain approach on LBPs (PLBP). Obtaining more accuracy by using more pyramid representations is an important result of PLBP, which increases not only feature dimensionality, but also classification computational time (CT). This study illustrates that more pyramid image representations will not improve the performance of the PLBP. We evaluate efficient levels of representation for the PLBP descriptor. In addition, the authors propose some feature selection approaches, such as the multi‐level and multi‐resolution (ML + MR) approach and the ML, MR and multi‐band (ML + MR + MB) approach and discuss their efficiency and CT. Experimental results show that the proposed feature selection approaches improve the accuracy of texture classification with fewer pyramid image representations. In addition, replacing the Chi‐2 similarity measurement with Czekannowski improves the accuracy of texture classification.

In this paper, we extend pyramid transform domain approach on local binary pattern (PLBP) to make a high-accurate and noise-tolerant texture descriptor. We combine PLBP information of sub-band images, which are attained using wavelet transform, in different resolution and make some new descriptors. Multi-level and -resolution LBP(MPR_LBP), multi-level and -band LBP (MPB_LBP), and multi-level, -band and -resolution LBP (MPBR_LBP) are our proposed descriptors that are applied to unsupervised classification of texture images on Outex, UIUC, and Scene-13 data sets. Experimental results show that the proposed descriptors not only demonstrate acceptable texture classification accuracy with significantly lower feature length, but also they are more noise-robustness to a number of recent state-of-the-art LBP extensions.