TY - GEN
T1 - Performance analysis of video segmentation
AU - Aditya, H.
AU - Gayatri, T.
AU - Santosh, T.
AU - Ankalaki, Shilpa
AU - Majumdar, Jharna
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/8/22
Y1 - 2017/8/22
N2 - Segmentation plays a vital role in digital media processing, pattern recognition and computer vision. In the last four decades, extensive research has been done and a number of algorithms have been published in the literature. Each one has its own merits and demerits. This paper aims to make a comparative analysis of the most popularly known segmentation methods, namely K-Means, Region Growing, Mean shift and Watershed segmentation for video from different category. The contribution of the paper is twofold: Conventionally, the value of K in K-Means segmentation is not known a prior and given as input. In order to avoid manual input by the user, Region growing segmentation is used. The prominent regions come as output of the region growing method, is used as input for K-Means segmentation. The performance of the segmentation algorithms is determined using a set of Quality Metric (QM) parameters. Segmentation is done on RGB Color Video from Entertainment, Sports and Natural Scenery category. The results show the most suitable algorithm for segmentation for each category of video. UBUNTU C Version 16.04 LTS is used to implement the algorithms.
AB - Segmentation plays a vital role in digital media processing, pattern recognition and computer vision. In the last four decades, extensive research has been done and a number of algorithms have been published in the literature. Each one has its own merits and demerits. This paper aims to make a comparative analysis of the most popularly known segmentation methods, namely K-Means, Region Growing, Mean shift and Watershed segmentation for video from different category. The contribution of the paper is twofold: Conventionally, the value of K in K-Means segmentation is not known a prior and given as input. In order to avoid manual input by the user, Region growing segmentation is used. The prominent regions come as output of the region growing method, is used as input for K-Means segmentation. The performance of the segmentation algorithms is determined using a set of Quality Metric (QM) parameters. Segmentation is done on RGB Color Video from Entertainment, Sports and Natural Scenery category. The results show the most suitable algorithm for segmentation for each category of video. UBUNTU C Version 16.04 LTS is used to implement the algorithms.
UR - https://www.scopus.com/pages/publications/85030221609
UR - https://www.scopus.com/pages/publications/85030221609#tab=citedBy
U2 - 10.1109/ICACCS.2017.8014567
DO - 10.1109/ICACCS.2017.8014567
M3 - Conference contribution
AN - SCOPUS:85030221609
T3 - 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017
BT - 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017
Y2 - 6 January 2017 through 7 January 2017
ER -