scholarly journals Indexing of American Football Video Using MPEG-7 Descriptors And MFCC Features

2021 ◽  
Author(s):  
Syed G Quadri
Keyword(s):  
Motion Vector ◽  
American Football ◽  
Application System ◽  
Linear Discriminant ◽  
Motion Activity ◽  
Classification Technique ◽  
Starting Point ◽  
Extra Point ◽  
Leave One Out ◽  
Sample Set

In this work, an application system is proposed to classify American Football Video shots. The application uses MPEG-7 motion and audio descriptors along with MEL Frequency Cepstrum coefficient features to classify the video shots into 4 categories, namely: Pass plays, Run plays, Field Goal/Extra Point plays and Kickoff/Punt plays. Fisher's Linear Discriminant Analysis is used to classify the 4 events, using a leave-one-out classification technique in order to minimize the sample set bias. For a database of 200 video shots taken from four different games, an overall system performance of 92.5% was recorded. In comparison to other American Football indexing systems, the proposed system performs 8% to 12% better. We have also proposed an algorithm that uses MPEG-7 motion activity descriptors and mean of the motion vector magnitudes, in a collaborative manner to detect the starting point of play events within video shots. The algorithm can detect starting points of the play with 83% accuracy.

scholarly journals Indexing of American Football Video Using MPEG-7 Descriptors And MFCC Features

2021 ◽  
Author(s):  
Syed G Quadri
Keyword(s):  
Motion Vector ◽  
American Football ◽  
Application System ◽  
Linear Discriminant ◽  
Motion Activity ◽  
Classification Technique ◽  
Starting Point ◽  
Extra Point ◽  
Leave One Out ◽  
Sample Set

In this work, an application system is proposed to classify American Football Video shots. The application uses MPEG-7 motion and audio descriptors along with MEL Frequency Cepstrum coefficient features to classify the video shots into 4 categories, namely: Pass plays, Run plays, Field Goal/Extra Point plays and Kickoff/Punt plays. Fisher's Linear Discriminant Analysis is used to classify the 4 events, using a leave-one-out classification technique in order to minimize the sample set bias. For a database of 200 video shots taken from four different games, an overall system performance of 92.5% was recorded. In comparison to other American Football indexing systems, the proposed system performs 8% to 12% better. We have also proposed an algorithm that uses MPEG-7 motion activity descriptors and mean of the motion vector magnitudes, in a collaborative manner to detect the starting point of play events within video shots. The algorithm can detect starting points of the play with 83% accuracy.

scholarly journals Discriminant Analysis of Traditional Chinese Medicinal Properties Based on Holistic Chemical Profiling by 1H-NMR Spectrometry

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Jie Zhang ◽  
Wenna Guo ◽  
Qiao Li ◽  
Faxin Sun ◽  
Xiaomeng Xu ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Chinese Medicine ◽  
Principal Component ◽  
Linear Discriminant ◽  
Chemical Profiling ◽  
H Nmr ◽  
Nmr Spectrometry ◽  
Cinnamomi Cortex ◽  
Medicinal Properties ◽  
Leave One Out

Medicinal property, which is closely related to drug chemical profiling, is the essence of traditional Chinese medicine (TCM) theory and has always been the focus of modern Chinese medicine. Based on dozens of classic and commonly used TCM herbs with recognized medicinal properties, the present study just aimed to investigate the feasibility and reliability of medicinal property discriminant by using 1H-NMR spectrometry, which provided a mass of spectral data showing holistic chemical profile for multivariate analysis and data mining, including principal component analysis (PCA), Fisher linear discriminant analysis (FLDA), and canonical discriminant analysis (CDA). By using FLDA for two-class recognition, a large majority of test herbs (59/61) were properly discriminated as cold or hot group, and the only two exceptions were Chuanbeimu (Fritillariae Cirrhosae Bulbus) and Rougui (Cinnamomi Cortex), suggesting that medicinal properties interrelate with flavor and body tropism, and all these factors together bring up medicinal property and efficacy. While by performing CDA, 98.4% of the original grouped herbs and 77.0% of the leave-one-out cross-validated grouped cases were correctly classified. The findings demonstrated that discriminant analysis based on holistic chemical profiling data by 1H-NMR spectrometry may provide a powerful alternative to have a deeper understanding of TCM medicinal property.

scholarly journals ADAPTIVE VARIABLE EXTRACTIONS WITH LDA FOR CLASSIFICATION OF MIXED VARIABLES, AND APPLICATIONS TO MEDICAL DATA

2021 ◽  
Vol 20 (Number 3) ◽  
pp. 305-327
Author(s):  
Hashibah Hamid ◽  
Nor Idayu Mahat ◽  
Safwati Ibrahim
Keyword(s):  
Multiple Correspondence Analysis ◽  
Principal Component ◽  
Medical Data ◽  
Significant Shift ◽  
Continuous Variables ◽  
Linear Discriminant ◽  
Mixed Variables ◽  
Constant Change ◽  
Leave One Out ◽  
Direct Use

The strategy surrounding the extraction of a number of mixed variables is examined in this paper in building a model for Linear Discriminant Analysis (LDA). Two methods for extracting crucial variables from a dataset with categorical and continuous variables were employed, namely, multiple correspondence analysis (MCA) and principal component analysis (PCA). However, in this case, direct use of either MCA or PCA on mixed variables is impossible due to restrictions on the structure of data that each method could handle. Therefore, this paper executes some adjustments including a strategy for managing mixed variables so that those mixed variables are equivalent in values. With this, both MCA and PCA can be performed on mixed variables simultaneously. The variables following this strategy of extraction were then utilised in the construction of the LDA model before applying them to classify objects going forward. The suggested models, using three real sets of medical data were then tested, where the results indicated that using a combination of the two methods of MCA and PCA for extraction and LDA could reduce the model’s size, having a positive effect on classifying and better performance of the model since it leads towards minimising the leave-one-out error rate. Accordingly, the models proposed in this paper, including the strategy that was adapted was successful in presenting good results over the full LDA model. Regarding the indicators that were used to extract and to retain the variables in the model, cumulative variance explained (CVE), eigenvalue, and a non-significant shift in the CVE (constant change), could be considered a useful reference or guideline for practitioners experiencing similar issues in future.

scholarly journals Rapid Identification of COVID-19 Severity in CT Scans through Classification of Deep Features

2020 ◽  
Author(s):  
Zekuan Yu ◽  
Xiaohu Li ◽  
Haitao Sun ◽  
Jian Wang ◽  
Tongtong Zhao ◽  
...  
Keyword(s):  
Cross Validation ◽  
Rapid Identification ◽  
Ct Scans ◽  
Accurate Identification ◽  
Linear Discriminant ◽  
Holdout Validation ◽  
Novel Coronavirus ◽  
Leave One Out ◽  
Fold Cross Validation

Abstract Background: To implement the real-time diagnosis of the severity of patients infected with novel coronavirus 2019 (COVID-19) and guide the follow-up therapeutic treatment, We collected chest CT scans of 202 patients diagnosed with the COVID-19 from three hospitals in Anhui Province, China.Methods: A total of 729 2D axial plan slices with 246 severe cases and 483 non-severe cases were employed in this study. Four pre-trained deep models (Inception-V3, ResNet-50, ResNet-101, DenseNet-201) with multiple classifiers (linear discriminant, linear SVM, cubic SVM, KNN and Adaboost decision tree) were applied to identify the severe and non-severe COVID-19 cases. Three validation strategies (holdout validation, 10-fold cross-validation and leave-one-out) are employed to validate the feasibility of proposed pipelines. Results and conclusion: The experimental results demonstrate that classification of the features from pre-trained deep models show the promising application in COVID-19 screening whereas the DenseNet-201 with cubic SVM model achieved the best performance. Specifically, it achieved the highest severity classification accuracy of 95.20% and 95.34% for 10-fold cross-validation and leave-one-out, respectively. The established pipeline was able to achieve a rapid and accurate identification of the severity of COVID-19. This may assist the physicians to make more efficient and reliable decisions.

scholarly journals A Comparison of Classification Techniques to Predict Brain-Computer Interfaces Accuracy Using Classifier-Based Latency Estimation

2020 ◽  
Vol 10 (10) ◽  
pp. 734
Author(s):  
Md Rakibul Mowla ◽  
Jesus D. Gonzalez-Morales ◽  
Jacob Rico-Martinez ◽  
Daniel A. Ulichnie ◽  
David E. Thompson
Keyword(s):  
Discriminant Analysis ◽  
System Performance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Linear Discriminant ◽  
Computer Interfaces ◽  
Classification Technique ◽  
Latency Jitter ◽  
Bci System

P300-based Brain-Computer Interface (BCI) performance is vulnerable to latency jitter. To investigate the role of latency jitter on BCI system performance, we proposed the classifier-based latency estimation (CBLE) method. In our previous study, CBLE was based on least-squares (LS) and stepwise linear discriminant analysis (SWLDA) classifiers. Here, we aim to extend the CBLE method using sparse autoencoders (SAE) to compare the SAE-based CBLE method with LS- and SWLDA-based CBLE. The newly-developed SAE-based CBLE and previously used methods are also applied to a newly-collected dataset to reduce the possibility of spurious correlations. Our results showed a significant (p<0.001) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, we also examined the effect of the number of electrodes on each classification technique. Our results showed that on the whole, CBLE worked regardless of the classification method and electrode count; by contrast the effect of the number of electrodes on BCI performance was classifier dependent.

Estimating player value in American football using plus–minus models

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
R. Paul Sabin
Keyword(s):  
Quantitative Analysis ◽  
Field Performance ◽  
American Football ◽  
Football Players ◽  
Model Framework ◽  
Sports Analytics ◽  
Starting Point ◽  
Out Of Sample ◽  
Sample Testing ◽  
Player Performance

Abstract Calculating the value of football player’s on-field performance has been limited to scouting methods while data-driven methods are mostly limited to quarterbacks. A popular method to calculate player value in other sports are Adjusted Plus–Minus (APM) and Regularized Adjusted Plus–Minus (RAPM) models. These models have been used in other sports, most notably basketball (Rosenbaum, D. T. 2004. Measuring How NBA Players Help Their Teams Win. http://www.82games.com/comm30.htm#_ftn1; Kubatko, J., D. Oliver, K. Pelton, and D. T. Rosenbaum. 2007. “A Starting Point for Analyzing Basketball Statistics.” Journal of Quantitative Analysis in Sports 3 (3); Winston, W. 2009. Player and Lineup Analysis in the NBA. Cambridge, Massachusetts; Sill, J. 2010. “Improved NBA Adjusted +/− Using Regularization and Out-Of-Sample Testing.” In Proceedings of the 2010 MIT Sloan Sports Analytics Conference) to estimate each player’s value by accounting for those in the game at the same time. Football is less amenable to APM models due to its few scoring events, few lineup changes, restrictive positioning, and small quantity of games relative to the number of teams. More recent methods have found ways to incorporate plus–minus models in other sports such as Hockey (Macdonald, B. 2011. “A Regression-Based Adjusted Plus-Minus Statistic for NHL players.” Journal of Quantitative Analysis in Sports 7 (3)) and Soccer (Schultze, S. R., and C.-M. Wellbrock. 2018. “A Weighted Plus/Minus Metric for Individual Soccer Player Performance.” Journal of Sports Analytics 4 (2): 121–31 and Matano, F., L. F. Richardson, T. Pospisil, C. Eubanks, and J. Qin (2018). Augmenting Adjusted Plus-Minus in Soccer with Fifa Ratings. arXiv preprint arXiv:1810.08032). These models are useful in coming up with results-oriented estimation of each player’s value. In American football, many positions such as offensive lineman have no recorded statistics which hinders the ability to estimate a player’s value. I provide a fully hierarchical Bayesian plus–minus (HBPM) model framework that extends RAPM to include position-specific penalization that solves many of the shortcomings of APM and RAPM models in American football. Cross-validated results show the HBPM to be more predictive out of sample than RAPM or APM models. Results for the HBPM models are provided for both Collegiate and NFL football players as well as deeper insights into positional value and position-specific age curves.

scholarly journals Greek Graviera Cheese Assessment through Elemental Metabolomics—Implications for Authentication, Safety and Nutrition

Molecules ◽  
2019 ◽  
Vol 24 (4) ◽  
pp. 670 ◽  
Author(s):  
Georgios Danezis ◽  
Charis Theodorou ◽  
Theofilos Massouras ◽  
Evangelos Zoidis ◽  
Ioannis Hadjigeorgiou ◽  
...  
Keyword(s):  
Geographic Origin ◽  
Precious Metals ◽  
Cow Milk ◽  
Linear Discriminant ◽  
Low Levels ◽  
Almost All ◽  
Sample Set ◽  
Larger Sample ◽  
Type Classification

This study presents the comprehensive elemental profile of Greek Graviera (Gruyère) cheeses. In total, 105 samples from nine different geographic regions produced from sheep, goat and cow milk and their mixtures were assessed. Elemental signatures of 61 elements were investigated for determination of geographic origin and milk type. Regional and milk type classification through Linear Discriminant Analysis was successful for almost all cases, while a less optimistic cross validation exercise presented lower classification rates. That points to further research using a much larger sample set, increasing confidence for cheese authentication utilizing also bioinformatics tools under development. This is the first study reporting signatures of 61 elements in dairy products including all sixteen rare earth elements and all seven precious metals. Safety and quality were assessed regarding toxic and nutritive elements. According to both EU and USA regulations and directives, Graviera is a nutritional source for trace and macro elements with low levels of toxic elements.

Measuring and analyzing color and texture information in anatomical leaf cross sections: an approach using computer vision to aid plant species identification

Botany ◽  
2011 ◽  
Vol 89 (7) ◽  
pp. 467-479 ◽  
Author(s):  
Jarbas Joaci de M. Sá Junior ◽  
André R. Backes ◽  
Davi Rodrigo Rossatto ◽  
Rosana M. Kolb ◽  
Odemir M. Bruno
Keyword(s):  
Cross Sections ◽  
Computational Approach ◽  
Information Structures ◽  
Plant Identification ◽  
Linear Discriminant ◽  
Central Brazil ◽  
Phylogenetic Studies ◽  
Leaf Tissues ◽  
Plant Species Identification ◽  
Leave One Out

Currently, studies on leaf anatomy have provided an important source of characters helping taxonomic, systematic, and phylogenetic studies. These studies strongly rely on measurements of characters (such as tissue thickness) and qualitative information (structures description, presence–absence of structures). In this work, we provide a new computational approach that semiautomates the collection of some quantitative data (cuticle, adaxial epidermis, and total leaf thickness) and accesses a new source of information in leaf cross-section images: the texture and the color of leaf tissues. Our aim was to evaluate this information for plant identification purposes. We successfully tested our system identifying eight species from different phylogenetic positions in the angiosperm phylogeny from the neotropical savanna of central Brazil. The proposed system checks the potential of identifying the species for each extracted measure using the Jeffrey–Matusita distance and composes a feature vector with the most important metrics. A linear discriminant analysis with leave-one-out to classify the samples was used. The experiments achieved a 100% success rate in terms of identifying the studied species accessing the above-described parameters, demonstrating that our computational approach can be a helpful tool for anatomical studies, especially ones devoted to plant identification and systematic studies.

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