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Graphical model representation of IAGM. Symbols in circles denote random variables; while the ones in squares denote model parameters. Plates indicate repetition (with the number of repetitions in the lower right), and arcs describe the conditional dependencies between the variables
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Data clustering is a fundamental unsupervised learning approach that impacts several domains such as data mining, computer vision, information retrieval, and pattern recognition. In this work, we develop a statistical framework for data clustering which uses Dirichlet processes and asymmetric Gaussian distributions. The parameters of this framework...
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Citations
... Zhang and Zhong proposed a DSP and FPGA-based embedded basketball sports video target tracking algorithm for the sports target tracking problem in basketball games [12]. Song et al. proposed a new method of using neural networks to predict the matchwinner, and the proposed new prediction method was to be reliable through testing [13]. Kusakunniran proposed a new sports training sports video analysis system with very high accuracy and comprehensiveness of video and image information analysis and high accuracy of keyframe extraction as well as recall rate [14]. ...
In this paper, we conduct an in-depth study and analysis of sports video recognition by improved hidden Markov model. The feature module is a complex gesture recognition module based on hidden Markov model gesture features, which applies the hidden Markov model features to gesture recognition and performs the recognition of complex gestures made by combining simple gestures based on simple gesture recognition. The combination of the two modules forms the overall technology of this paper, which can be applied to many scenarios, including some special scenarios with high-security levels that require real-time feedback and some public indoor scenarios, which can achieve different prevention and services for different age groups. With the increase of the depth of the feature extraction network, the experimental effect is enhanced; however, the two-dimensional convolutional neural network loses temporal information when extracting features, so the three-dimensional convolutional network is used in this paper to extract features from the video in time and space. Multiple binary classifications of the extracted features are performed to achieve the goal of multilabel classification. A multistream residual neural network is used to extract features from video data of three modalities, and the extracted feature vectors are fed into the attention mechanism network, then, the more critical information for video recognition is selected from a large amount of spatiotemporal information, further learning the temporal dependencies existing between consecutive video frames, and finally fusing the multistream network outputs to obtain the final prediction category. By training and optimizing the model in an end-to-end manner, recognition accuracies of 92.7% and 64.4% are achieved on the dataset, respectively.
Many real-life problems are stated as nonlabeled high-dimensional data. Current strategies to select features are mainly focused on labeled data, which reduces the options to select relevant features for unsupervised problems, such as clustering. Recently, feature saliency models have been introduced and developed as clustering models to select and detect relevant variables/features as the model is learned. Usually, these models assume that all variables are independent, which narrows their applicability. This article introduces asymmetric hidden Markov models with feature saliencies, i.e., models capable of simultaneously determining during their learning phase relevant variables/features and probabilistic relationships between variables. The proposed models are compared with other state-of-the-art approaches using synthetic data and real data related to grammatical face videos and wear in ball bearings. We show that the proposed models have better or equal fitness than other state-of-the-art models and provide further data insights.
