Mendeley
TY  _  JOUR
ID  -  2021071016507
TI  -  Foreground-Back ground Segmentation using K-Means Clustering Algorithm and Support Vector Machine
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -  Rezaei Masoumeh
AU  -  rezaei mansoureh
AU  -  Rezaei Masoud
AD  -  دانشکده برق و کامپیوتر، دانشگاه سیستان و بلوچستان 
AD  -  پردیس فنی و مهندسی 
AD  -  Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
Y1  -  2023
PY  -  2023
VL  -  41
IS  -  
SP  -  65
EP  -  74
KW  -  Foreground-Background Segmentation
KW  -   Support vector machine
KW  -   k-means clustering
KW  -   saliency map
DO  -  
N2  -  Foreground-background image segmentation has been an important research problem. It is one of the main tasks in the field of computer vision whose purpose is detecting variations in image sequences. It provides candidate objects for further attentional selection, e.g., in video surveillance. In this paper, we introduce an automatic and efficient Foreground-background segmentation. The proposed method starts with the detection of visually salient image regions with a saliency map that uses Fourier transform and a Gaussian filter.  Then, each point in the maps classifies as salient or non-salient using a binary threshold. Next, a hole filling operator is applied for filling holes in the achieved image, and the area-opening method is used for removing small objects from the image. For better separation of the foreground and background, dilation and erosion operators are also used. Erosion and dilation operators are applied for shrinking and expanding the achieved region. Afterward, the foreground and background samples are achieved. Because the number of these data is large, K-means clustering is used as a sampling technique to restrict computational efforts in the region of interest. K cluster centers for each region are set for training of Support Vector Machine (SVM). SVM, as a powerful binary classifier, is used to segment the interest area from the background. The proposed method is applied on a benchmark dataset consisting of 1000 images and experimental results demonstrate the supremacy of the proposed method to some other foreground-background segmentation methods in terms of ER, VI, GCE, and PRI.
UR  - rimag.ir/en/Article/16507
L1  - rimag.ir/en/Article/Download/16507
ER  -  
