NDLI: Classification via ensembles of basic thresholding classifiers

Content Provider	IET Digital Library
Author	Toksöz, Mehmet Altan Ulusoy, İlkay
Abstract	The authors present a sparsity-based algorithm, basic thresholding classifier (BTC), for classification applications which is capable of identifying test samples extremely rapidly and performing high classification accuracy. They introduce a sufficient identification condition (SIC) under which BTC can identify any test sample in the range space of a given dictionary. By using SIC, they develop a procedure which provides a guidance for the selection of threshold parameter. By exploiting rapid classification capability, they propose a fusion scheme in which individual BTC classifiers are combined to produce better classification results especially when very small number of features is used. Finally, they propose an efficient validation technique to reject invalid test samples. Numerical results in face identification domain show that BTC is a tempting alternative to sparsity-based classification algorithms such as greedy orthogonal matching pursuit and l 1-minimisation.
Starting Page	433
Ending Page	442
Page Count	10
ISSN	17519632
Volume Number	10
e-ISSN	17519640
Issue Number	Issue 5, Aug (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/10/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2015.0077
Journal	IET Computer Vision
Publisher Date	2016-02-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Approximation Theory Basic Thresholding Classifier BTC Classifier Computer Vision And Image Processing Technique Face Identification Domain Face Recognition Fusion Scheme Greedy Algorithm Greedy Orthogonal Matching Pursuit Image Classification Image Fusion Image Recognition Interpolation And Function Approximation Iterative Method L1-minimisation Minimisation Numerical Analysis Optimisation Technique Sensor Fusion SIC Sparsity-based Classification Algorithm Sufficient Identification Condition Threshold Parameter Selection
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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