International Journal of Pattern Recognition and Artificial Intelligence

Traditional pattern recognition is based on “optimal partition” and the goal is to find an optimal classification interface based on the distribution of each category in high-dimensional space, thus has its inherent shortcomings and deficiencies. While topology pattern recognition can effectively compensate for the shortcomings of traditional pattern recognition, topological pattern recognition is based on “cognition” and the goal is to find the appropriate cover according to the “complex set cover” in high-dimensional space to achieve cognitive effect. Topological pattern recognition can effectively consummate the characteristics of high error rate, low recognition rate and repetitive training in the existing recognition system with low training sample number. At present, topology pattern recognition has been applied in many areas of social life. However, one problem that can’t be ignored is that topological pattern recognition requires a long training time and low fault tolerance rate. Therefore, this paper proposes an improved multidimensional–multiresolution topological pattern recognition, and applies it to text classification and recognition. The results show that the improved multidimensional–multiresolution topological pattern recognition method can effectively reduce the training time of text classification and improve the classification efficiency.

High-dimensional data is ubiquitous in scientific research and industrial production fields. It brings a lot of information to people, at the same time, because of its sparse and redundancy, it also brings great challenges to data mining and pattern recognition. Dimensionality reduction can reduce redundancy and noise, reduce the complexity of learning algorithms, and improve the accuracy of classification, it is an important and key step in pattern recognition system. In this paper, we overview the classical techniques for dimensionality reduction and review their properties, and categorize these techniques according to their implementation process. We deduce each algorithm in detail and intuitively show their underlying mathematical principles. Thereby, the focus is to uncover the optimization process for each technique. We compare the characteristics and limitations of each technique and summarize the scope of application, discussing a number of open problems and a perspective of research trend in future.

Although existing sparse restricted Boltzmann machine (SRBM) can make some hidden units activated, the major disadvantage is that the sparseness of data distribution is usually overlooked and the reconstruction error becomes very large after the hidden unit variables become sparse. Different from the SRBMs which only incorporate a sparse constraint term in the energy function formula from the original restricted Boltzmann machine (RBM), an energy function constraint SRBM (ESRBM) is proposed in this paper. The proposed ESRBM takes into account the sparseness of the data distribution so that the learned features can better reflect the intrinsic features of data. Simulations show that compared with SRBM, ESRBM has smaller reconstruction error and lower computational complexity, and that for supervised learning classification, ESRBM obtains higher accuracy rates than SRBM, classification RBM, and Softmax classifier.

We proposed an improved variational Bayesian exploration-based active Sarsa (VBE-ASAR) algorithm, which tries to balance the exploration and exploitation dilemma, and speeds up the convergence rate. First, in the learning process, variational Bayesian method is adopted to measure the information gain, which is used as an exploration factor to construct an internal reward function for heuristic exploration. In addition, before the learning process, in order to improve the exploration performance, transfer learning is used to initialize the value function, where Bisimulation metric is introduced to measure the distance between two states from the source MDP and the target MDP, respectively. Finally, we apply the proposed algorithm to the cliff walking problem, and compare with the Sarsa algorithm, the Q-Learning algorithm, the VFT-Sarsa algorithm and the Bayesian Sarsa (BS) algorithm. Experimental results show that the VBE-ASAR algorithm has a faster learning rate.

Subspace learning has been widely utilized to extract discriminative features for classification task, such as face recognition, even when facial images are occluded or corrupted. However, the performance of most existing methods would be degraded significantly in the scenario of that data being contaminated with severe noise, especially when the magnitude of the gross corruption can be arbitrarily large. To this end, in this paper, a novel discriminative subspace learning method is proposed based on the well-known low-rank representation (LRR). Specifically, a discriminant low-rank representation and the projecting subspace are learned simultaneously, in a supervised way. To avoid the deviation from the original solution by using some relaxation, we adopt the Schatten $p$-norm and ${\ell }_p$-norm, instead of the nuclear norm and ${\ell }_1$-norm, respectively. Experimental results on two famous databases, i.e. PIE and ORL, demonstrate that the proposed method achieves better classification scores than the state-of-the-art approaches.

The online handwriting recognition of Tibetan characters is still in its infancy. For further research, an online handwriting database of large Tibetan character set was developed, and a recognition research was carried out on this database as a baseline result. The Northwest Minzu University Online Tibetan Handwriting Database (NMU-OLTHWDB) contains 7240 different types of characters, and the sample number in each type is 5000. The total number of samples is $7240\times 5000$. The database covers Tibetan Character Collection, Information Technology Tibetan Coded Character set (Extension Set A), and Information Technology Tibetan Coded Character set (Extension Set B). The characters in the database are composed of 170 types of different components. We studied the online handwritten Tibetan recognition software also, and the character feature extraction, classifier training, and the statistics and analysis of the recognition results on the test set were mainly introduced. The character features included the direction attribute coefficients and spatial combination, and the feature matrix was compressed by Linear Discriminate Analysis (LDA). A quick classifier was designed by a modified quadratic discriminate function (QMQDF), and was trained with 4500 sets of samples. In the large character set, the recognition rates of top 1, top 3, top 5, and top 10 were 75.2%, 89.56%, 93.02%, and 95.96%, respectively. Moreover, an online handwriting recognition system for Tibetan large character set was designed with good performance.

There are two main techniques to convert written or printed text into digital format. The first technique is to create an image of written/printed text, but images are large in size so they require huge memory space to store, as well as text in image form cannot be undergo further processes like edit, search, copy, etc. The second technique is to use an Optical Character Recognition (OCR) system. OCR’s can read documents and convert manual text documents into digital text and this digital text can be processed to extract knowledge. A huge amount of Urdu language’s data is available in handwritten or in printed form that needs to be converted into digital format for knowledge acquisition. Highly cursive, complex structure, bi-directionality, and compound in nature, etc. make the Urdu language too complex to obtain accurate OCR results. In this study, supervised learning-based OCR system is proposed for Nastalique Urdu language. The proposed system evaluations under a variety of experimental settings apprehend 98.4% training results and 97.3% test results, which is the highest recognition rate ever achieved by any Urdu language OCR system. The proposed system is simple to implement especially in software front of OCR system also the proposed technique is useful for printed text as well as handwritten text and it will help in developing more accurate Urdu OCR’s software systems in the future.

Based on the cognition of the physiological structure of the human visual system, this paper considers that the mechanism of the human visual system to perceive the image color appearance includes the adaptive mechanism of the retinal photoreceptor to the ambient light and the spatial frequency response mechanism of the Neuronal receptive field in the visual pathway. In this paper, we first provide the computing framework of the image color appearance model related to human cognitive process, then propose using Gabor wavelet as the basis function of the visual nerve cells response to apply CIECAT02 model to the calculation of image color adaption and to simulate the multi-scale superposition of human visual spatial frequency tuning curves, and finally accomplish the development of the algorithm for predicting image color appearance. The results show that the prediction algorithm proposed in this paper is closer to the visual perception of human eyes than the similar algorithm.

Flower plants have become a major difficulty in virtual plant research because of their rich external morphological structure and complex physiological processes. Computer vision simulation provides powerful tools for exploring powerful biological systems and operating laws. In this paper, Chrysanthemum and Chinese rose, double flowers as the symbolic flowers of Beijing, are chosen as the study subject. On the basis of maximizing the protection of flower growth structure, an effective method based on laser scanning for three-dimensional (3D) reconstruction and visual simulation of flower plants is proposed. This method uses laser technology to scan the sample and store it as point cloud data. After applying a series of image analysis and processing techniques such as splicing, denoising, repairing and color correction, the digital data optimized by the sample is obtained accurately and efficiently, and a highly realistic 3D simulation model of the plant is formed. The results of the research indicate that it is a convenient research method for the 3D reconstruction of flower plants and computer vision simulation of virtual plants. It also provides an effective way for in-depth study of scientific experiments and digital protection of rare and endangered plants.

The early multi-point leakage source signals of urban gas pipeline are weak and can be easily affected by environmental noise and signal interference between adjacent sources, which causes large leakage positioning error. In this paper, an integrated signal processing method combining VMD, BSS and Relative entropy for multi-point pipeline leakage signal and source positioning is presented. Firstly, VMD and Relative entropy were employed to obtain effective IMF mode components and their features during the decomposition for pipeline leakage signal. Relative entropy was used to improve signal-to-noise ratio and extract the features of leakage signal. Then, BSS was used to decompose multi-point mixed leakage signals so as to obtain independent signal components. Finally, the time difference and wave velocity were, respectively, obtained by calculating the time domain distribution of the independent signal components and the main modal guided wave, so the precise positioning of the pipeline leakage was realized. The results show that the combined method proposed can not only select and extract leakage signal adaptively but also separate single independent signal from multi-point mixed signal, which helps to locate pipeline multi-point leakage sources more accurately.

In this paper, the artificial intelligence control algorithm for steering robot of steering wheel is studied. The steering movement of wheeled soccer robot is controlled by artificial intelligence control algorithm, and the steering movement is modeled and simulated. Firstly, the characteristics of artificial neurons are simulated and a similar control model is constructed to complete the simulation of football. The artificial intelligence control algorithm has a dynamic feedback item compared with the traditional intelligent model, which has a better effect on the steering control of the wheeled soccer robot. In this paper, artificial intelligence control algorithm is used to optimize the parameters of artificial intelligence control algorithm, and the output of control signal of each steering part of wheeled soccer robot is simulated in the experiment, and the control of the steering action of wheeled soccer robot by artificial intelligence control algorithm is verified by experiments. Then the artificial intelligence control algorithm forms the connection structure. This method provides a good reference for steering control of wheeled soccer robots.

Fuzzy support vector machine (FSVM) is a part of machine learning with its good classification effect. So far, there are two most commonly used FSVM models: FSVM on account of class core and fuzzy support vector machine on account of hyperplane that is over class core. Each has its own problems: FSVM on account of class core are dependent on the geometric shape of sample sets. Although FSVM on account of hyperplane that is over class core can solve the above problems to some extent. However, this algorithm has low generalization ability and high time complexity. Therefore, Inspired by these two common models, the paper proposes an improved membership function method. By analyzing and calculating the potential support vector sample points, adjustment factor is obtained, which drives the class core to adjust along the direction away from the outliers. In this way, membership of noise and outliers are reduced and the membership of support vector will also increase to some extent. In this paper, a new experimental comparison method is used, which can make the comparison of classification effect more obvious and convincing. The experimental part compares the proposed FSVM model with the above two FSVM models. It shows that the proposed algorithm improves the stability and classification accuracy to some extent.

The entropic lattice Boltzmann method (ELBM) is an excellent method of numerical stability among of different versions of the lattice Boltzmann method for the simulation of hydrodynamics, especially for wind field simulation application of power grid disaster conditions. In this paper, an efficient improved particle swarm optimization (PSO) algorithm is studied for optimizing calculation parameters to achieve load balancing of ELBM on nonuniform grids in a heterogeneous computing platform. We also introduce a new concept of multi-block ELBM on composite grids for realization of the ELBM simulations of incompressible driven cavity flow. These new approaches rely on a two-dimensional space-time interpolation and solving the relaxation time parameter by direct approximation optimization strategy to guarantee conservation. Our CPU–GPU implementation of multi-block ELBM based on the improved PSO algorithm not only exploits adequately multi-core CPU computing resources for load balancing, but also follows carefully optimized storage to increase coalesced access on a GPU platform. The three-dimensional-driven cavity flow simulations validate the proposed multi-block ELBM even with severely under-resolved grids. In addition, some performance metrics are investigated based on the implementations of different refined grids and threading blocks. These results exhibit the improved PSO algorithm of the ELBM method which can optimize computing resource parameters in heterogeneous platforms, and the present multi-block ELBM can substantially improve the accuracy and computational efficiency for viscous flow computations.