Optimized K-Means Algorithm
Joint Authors
Belhaouari, Samir Brahim
Ahmed, Shahnawaz
Mansour, Samer
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-07
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
The localization of the region of interest (ROI), which contains the face, is the first stepin any automatic recognition system, which is a special case of the face detection.
However, facelocalization from input image is a challenging task due to possible variations in location, scale, pose,occlusion, illumination, facial expressions, and clutter background.
In this paper we introduce a newoptimized k-means algorithm that finds the optimal centers for each cluster which corresponds to theglobal minimum of the k-means cluster.
This method was tested to locate the faces in the input imagebased on image segmentation.
It separates the input image into two classes: faces and nonfaces.
Toevaluate the proposed algorithm, MIT-CBCL, BioID, and Caltech datasets are used.
The results showsignificant localization accuracy.
American Psychological Association (APA)
Belhaouari, Samir Brahim& Ahmed, Shahnawaz& Mansour, Samer. 2014. Optimized K-Means Algorithm. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-1044319
Modern Language Association (MLA)
Belhaouari, Samir Brahim…[et al.]. Optimized K-Means Algorithm. Mathematical Problems in Engineering No. 2014 (2014), pp.1-14.
https://search.emarefa.net/detail/BIM-1044319
American Medical Association (AMA)
Belhaouari, Samir Brahim& Ahmed, Shahnawaz& Mansour, Samer. Optimized K-Means Algorithm. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-1044319
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1044319
