Soccer Video Structure Analysis by Parallel Feature Fusion Network and Hidden-to-Observable Transferring Markov Model

The aim of the paper is to propose a feature fusion based Audio-Visual Speaker Identification (AVSI) system with varied conditions of illumination environments. Among the different fusion strategies, feature level fusion has been used for the proposed AVSI system where Hidden Markov Model (HMM) is used for learning and classification. Since the feature set contains richer information about the raw biometric data than any other levels, integration at feature level is expected to provide better authentication results. In this paper, both Mel Frequency Cepstral Coefficients (MFCCs) and Linear Prediction Cepstral Coefficients (LPCCs) are combined to get the audio feature vectors and Active Shape Model (ASM) based appearance and shape facial features are concatenated to take the visual feature vectors. These combined audio and visual features are used for the feature-fusion. To reduce the dimension of the audio and visual feature vectors, Principal Component Analysis (PCA) method is used. The VALID audio-visual database is used to measure the performance of the proposed system where four different illumination levels of lighting conditions are considered. Experimental results focus on the significance of the proposed audio-visual speaker identification system with various combinations of audio and visual features.

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Analysis on the Structure and the Hidden Markov Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.1359 ◽

2013 ◽

Vol 846-847 ◽

pp. 1359-1363

Author(s):

Meng Liu ◽

Wei Zhou ◽

Yang Wang

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

High Speed ◽

Hidden Markov ◽

Rapid Development ◽

Slow Motion ◽

Differential Method ◽

Accurate Information ◽

Soccer Video ◽

Video Feature

With the rapid development of multimedia communications and the computer network technology, video information has a rising proportion in multimedia information. It owns the great amount of data so that it can obtain accurate information for needed video retrieval research, which has become one of the hot topics of research in this field. By taking soccer video for example, this paper firstly analyzes structure of the soccer video and framework structure. Based on this, this paper makes a specific analysis on the extraction of soccer video feature by combining with the literature to apply SSD apparent characteristic. And then, assisted by the improvement of Hidden Markov Model (HMM), this paper constructs the second frame differential method to detect the shot of soccer video. The test results show that the precision of this method is higher in close-up and misjudgment slow motion. However, it has certain leak detection in slow motion replay of high speed movement.

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Automated Atrial Fibrillation Detection Based on Feature Fusion Using Discriminant Canonical Correlation Analysis

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6691177 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Jingjing Shi ◽

Chao Chen ◽

Hui Liu ◽

Yinglong Wang ◽

Minglei Shu ◽

...

Keyword(s):

Atrial Fibrillation ◽

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Expert Knowledge ◽

Feature Fusion ◽

Intraclass Correlation ◽

Single Feature ◽

Disability Rate ◽

Parallel Feature

Atrial fibrillation (AF) is one of the most common cardiovascular diseases, with a high disability rate and mortality rate. The early detection and treatment of atrial fibrillation have great clinical significance. In this paper, a multiple feature fusion is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The proposed method uses discriminant canonical correlation analysis (DCCA) feature fusion. It fully takes intraclass correlation and interclass correlation into consideration and solves the problem of computation and information redundancy with simple series or parallel feature fusion. The DCCA integrates traditional features extracted by expert knowledge and deep learning features extracted by the residual network and gated recurrent unit network to improve the low accuracy of a single feature. Based on the Cardiology Challenge 2017 dataset, the experiments are designed to verify the effectiveness of the proposed algorithm. In the experiments, the F1 index can reach 88%. The accuracy, sensitivity, and specificity are 91.7%, 90.4%, and 93.2%, respectively.

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