Optimized K-Means Algorithm

被引：16
|
作者：
Belhaouari, Samir Brahim [1 ]
Ahmed, Shahnawaz [1 ]
Mansour, Samer [2 ]
机构：
[1] Alfaisal Univ, Coll Sci & Gen Studies, Dept Math & Comp Sci, Riyadh, Saudi Arabia
[2] Alfaisal Univ, Coll Engn, Dept Software Engn, Riyadh, Saudi Arabia
来源：
MATHEMATICAL PROBLEMS IN ENGINEERING | 2014年 / 2014卷
关键词：
MEANS CLUSTERING-ALGORITHM; INITIALIZATION; FACES;
D O I：
10.1155/2014/506480
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The localization of the region of interest (ROI), which contains the face, is the first step in any automatic recognition system, which is a special case of the face detection. However, face localization 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 new optimized k-means algorithm that finds the optimal centers for each cluster which corresponds to the global minimum of the k-means cluster. This method was tested to locate the faces in the input image based on image segmentation. It separates the input image into two classes: faces and nonfaces. To evaluate the proposed algorithm, MIT-CBCL, BioID, and Caltech datasets are used. The results show significant localization accuracy.
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收藏
页数：14
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