A comparative study on mixture of Gaussians for object segmentation

Khatoon, et al.

International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

Print ISSN: 2313-626X

Volume 4, Issue 6 (June 2017), Pages: 28-34

Title: A comparative study on mixture of Gaussians for object segmentation

Author(s): Robina Khatoon ¹, Syed Muhammad Saqlain Shah ^1,*, Shafina Bibi ¹, Imran Khan ¹, Muhammad Usman Ashraf ^{1, 2}

Affiliation(s):

¹Department of CS & SE, International Islamic University, Islamabad, Pakistan
²Institute of Business Management Sciences, University of Agriculture, Faisalabad, Pakistan

https://doi.org/10.21833/ijaas.2017.06.004

Full Text - PDF XML

Abstract:

Segmentation is the fundamental step in most of digital image processing and computer vision based applications for feature extraction. The purpose of segmentation is to partition an image into foreground and background. Numerous segmentation algorithms have been proposed for the last four decades ranging from degraded images; high and low contrast images, indoor video, outdoor videos, videos with static background and dynamic backgrounds. This paper presents evaluation and comparison of segmentation techniques used for real-time moving objects through static and adaptive number of Gaussians. The techniques are tested for both indoor and outdoor scenes. The comparison is presented on the basis of qualitative results and computational complexities.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Mixture of Gaussians, Segmentation, Foreground

Article History: Received 19 January 2017, Received in revised form 14 March 2017, Accepted 18 March 2017

Digital Object Identifier:

https://doi.org/10.21833/ijaas.2017.06.004

Citation:

Khatoon R, Shah SMS, Bibi S, Khan I, Ashraf MU (2017). A comparative study on mixture of Gaussians for object segmentation. International Journal of Advanced and Applied Sciences, 4(6): 28-34

http://www.science-gate.com/IJAAS/V4I6/Khatoon.html

References:

Atev S, Masoud O, and Papanikopoulos N (2004). Practical mixture of gaussians with brightness monitoring. In The 7th International IEEE Conference on Intelligent Transportation Systems, IEEE, Washington, USA: 423-428. https://doi.org/10.1109/ITSC.2004.1398937 https://doi.org/10.1109/itsc.2004.1398937

Balafar MA (2014). Gaussian mixture model based segmentation methods for brain MRI images. Artificial Intelligence Review, 41(3): 429-439. https://doi.org/10.1007/s10462-012-9317-3

Carlos C, García N, and Salgado L (2008). A new strategy based on adaptive mixture of gaussians for real-time moving objects segmentation. In the Conference on Real-Time Image Processing, International Society for Optics and Photonics (SPIE), USA. https://doi.org/10.1117/12.768139

Demirci R and Karaguuml T (2010). Light refraction based medical image segmentation. Scientific Research and Essays, 5(10):1127-1132.

Douglas R (2015). Gaussian mixture models. In: Li SZ and Jain AK (Eds.), Encyclopedia of biometrics: 827-832. Springer, USA.

KaewTraKulPong P and Bowden R (2002). An improved adaptive background mixture model for real-time tracking with shadow detection. In: Remagnino P, Jones GA, Paragios N, and Regazzoni CS (Eds.), Video-based surveillance systems: 134-144. Springer, USA.

Khatoon R, Saqlain SM, and Bibi S (2012). A robust and enhanced approach for human detection in crowd. In the 15th International Conference on Multitopic (INMIC'12), IEEE, Islamabad, Pakistan: 215-221. https://doi.org/10.1109/inmic.2012.6511457

Shah S and Chauhan NC (2015). An automated approach for segmentation of brain MR images using Gaussian mixture model based hidden markov random field with expectation maximization. Journal of Biomedical Engineering and Medical Imaging, 2(4): 58-70. https://doi.org/10.14738/jbemi.24.1411

Srikanth R, Narasimhan S, Basavaraj MG, and Dewar R (2016). Unsupervised segmentation of cervical cell images using Gaussian mixture model. In the 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR'16), IEEE, Las Vegas, USA: 70-75. https://doi.org/10.1109/CVPRW.2016.173 PMid:27639226

Stauffer C and Grimson W (1999). Adaptive background mixture models for real lime tracking. In the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Fort Collins, USA, 2: 246-252. https://doi.org/10.1109/CVPR.1999.784637

Yan M and Shui PL (2015). Interactive image segmentation based on Gaussian mixture models with spatial prior. International Journal of Multimedia and Ubiquitous Engineering, 10(7): 105-114. https://doi.org/10.14257/ijmue.2015.10.7.11

Yogameena B, Komagal E, Archana M, and RajuAbhaikumar S (2010). Support vector machine-based human behavior classification in crowd through projection and star skeletonization. Journal of Computer Science, 6: 1008-1013. https://doi.org/10.3844/jcssp.2010.1008.1013

Yogameena B, Veeralakshmi S, Komagal E, Raju S, and Abhaikumar V (2009). RVM-based human action classification in crowd through projection and star skeletonization. EURASIP Journal on Image and Video Processing, 2009: 164019. https://doi.org/10.1155/2009/164019

Zeng S, Rui H, Zhen K, and Nong S (2014). Image segmentation using spectral clustering of Gaussian mixture models. Neurocomputing, 144: 346-356. https://doi.org/10.1016/j.neucom.2014.04.037

Zezhi C and Ellis T (2014). A self-adaptive Gaussian mixture model. Computer Vision and Image Understanding, 122: 35-46. https://doi.org/10.1016/j.cviu.2014.01.004