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1887

access icon free Classification via ensembles of basic thresholding classifiers

© The Institution of Engineering and Technology
Received 09/03/2015, Accepted 16/11/2015, Revised 01/11/2015, Published 19/02/2016

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.

Inspec keywords: greedy algorithms; image classification; face recognition; image fusion; minimisation; iterative methods; approximation theory

Other keywords: sparsity-based classification algorithms; SIC; greedy orthogonal matching pursuit; fusion scheme; sufficient identification condition; basic thresholding classifiers; face identification domain; l1-minimisation; threshold parameter selection; BTC classifiers

Subjects: Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Sensor fusion; Computer vision and image processing techniques; Optimisation techniques; Image recognition; Optimisation techniques

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