Higher Order Spectra based Support Vector Machine for Arrhythmia Classification

2009 ◽  
pp. 231-234 ◽  
Author(s):  
K. C. Chua ◽  
V. Chandran ◽  
U. R. Acharya ◽  
C. M. Lim
Keyword(s):  
Support Vector Machine ◽  
Higher Order ◽  
Support Vector ◽  
Higher Order Spectra

scholarly journals Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images

2021 ◽  
Vol 18 (15) ◽  
pp. 8059
Author(s):  
Oliver Faust ◽  
Joel En Wei Koh ◽  
Vicnesh Jahmunah ◽  
Sukant Sabut ◽  
Edward J. Ciaccio ◽  
...  
Keyword(s):  
Brain Mri ◽  
Higher Order ◽  
Image Texture ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Stroke Severity ◽  
Gray Level ◽  
Anterior Circulation ◽  
Severity Classification ◽  
Higher Order Spectra

This paper presents a scientific foundation for automated stroke severity classification. We have constructed and assessed a system which extracts diagnostically relevant information from Magnetic Resonance Imaging (MRI) images. The design was based on 267 images that show the brain from individual subjects after stroke. They were labeled as either Lacunar Syndrome (LACS), Partial Anterior Circulation Syndrome (PACS), or Total Anterior Circulation Stroke (TACS). The labels indicate different physiological processes which manifest themselves in distinct image texture. The processing system was tasked with extracting texture information that could be used to classify a brain MRI image from a stroke survivor into either LACS, PACS, or TACS. We analyzed 6475 features that were obtained with Gray-Level Run Length Matrix (GLRLM), Higher Order Spectra (HOS), as well as a combination of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) methods. The resulting features were ranked based on the p-value extracted with the Analysis Of Variance (ANOVA) algorithm. The ranked features were used to train and test four types of Support Vector Machine (SVM) classification algorithms according to the rules of 10-fold cross-validation. We found that SVM with Radial Basis Function (RBF) kernel achieves: Accuracy (ACC) = 93.62%, SPE

scholarly journals Learning Non-Uniform Hypergraph for Multi-Object Tracking

2019 ◽  
Vol 33 ◽  
pp. 8981-8988 ◽  
Author(s):  
Longyin Wen ◽  
Dawei Du ◽  
Shengkun Li ◽  
Xiao Bian ◽  
Siwei Lyu
Keyword(s):  
Support Vector Machine ◽  
Object Tracking ◽  
State Of The Art ◽  
Higher Order ◽  
The State ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Uniform Hypergraph ◽  
Detection Scheme ◽  
Structural Support

The majority of Multi-Object Tracking (MOT) algorithms based on the tracking-by-detection scheme do not use higher order dependencies among objects or tracklets, which makes them less effective in handling complex scenarios. In this work, we present a new near-online MOT algorithm based on non-uniform hypergraph, which can model different degrees of dependencies among tracklets in a unified objective. The nodes in the hypergraph correspond to the tracklets and the hyperedges with different degrees encode various kinds of dependencies among them. Specifically, instead of setting the weights of hyperedges with different degrees empirically, they are learned automatically using the structural support vector machine algorithm (SSVM). Several experiments are carried out on various challenging datasets (i.e., PETS09, ParkingLot sequence, SubwayFace, and MOT16 benchmark), to demonstrate that our method achieves favorable performance against the state-of-the-art MOT methods.

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