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Robust Classification Based on Sparsity: Basics and Potentials of the Sparse Representation based Classifier

Battini Sönmez, Elena

Verlag: LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659400661 / ISBN 13: 9783659400667
Gebraucht / Soft cover / Anzahl: 1
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Titel: Robust Classification Based on Sparsity: ...

Verlag: LAP LAMBERT Academic Publishing

Erscheinungsdatum: 2013

Einband: Soft cover

Zustand: Used


This Book is in Good Condition. Clean Copy With Light Amount of Wear. 100% Guaranteed. Buchnummer des Verkäufers ABE_book_usedgood_3659400661

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Inhaltsangabe: Classification of images is one of the most challenging research topics in machine learning, with a range of application including computer vision. Classification of faces is particularly hard due to the presence of disturbance elements such as illumination, pose, misalignment, occlusion, low resolution, expressions and scale; classification of emotions is complicated by the different level of intensity, cultural changes, and the co-presence of identity related info. Recent developments in the theory of compressive sensing have inspired a sparsity based classification algorithm, which turns out to be very successful. This work summarizes the study done on the Sparse Representation based Classifier (SRC), it investigates the characteristics of SRC, and it tests its potentialities on 2D emotional faces. It is an empirical work; all experiments use the Extended Yale B and the Extended Cohn-Kanade databases. Experimental results place SRC into the shortlist of the most successful classifiers. This study should help shed some light on SRC and should be especially useful to researchers and professionals in machine learning and computer vision.

Über den Autor: BSc Computing Science, University of Pisa, IT; MSc Computing Software and System Design, University of Newcastle, U.K; PhD Computer Engineering, Yildiz Technical University, Istanbul, TR. Instructor at Computer Eng. department, Istanbul Bilgi University. Research areas: image processing and machine learning.

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