International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 4 | Issue 6 | Year 2013 | Article Id. IJCTT-V4I6P117 | DOI : https://doi.org/10.14445/22312803/IJCTT-V4I6P117

Background and Foreground Human Character Segments for Video Object Segmentation

Prof.K.Mahesh, B.Reka

Citation :

Prof.K.Mahesh, B.Reka, "Background and Foreground Human Character Segments for Video Object Segmentation," International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 6, pp. 1604-1608, 2013. Crossref, https://doi.org/10.14445/22312803/IJCTT-V4I6P117

Abstract

In this paper present the single human being character segmentation in video of foreground objects using genetic algorithm. The genetic algorithm is used to discover the foreground objects of single human character; this is occurred in video of moving time. Here our work is segment the single human character in video of moving or unmoving time. Then this paper segments the living things and non-living things using the k-means algorithm. And the background object is segmented in the video. This paper will also provide researchers and practitioners a comprehensive understanding of state-of-the-art of video segmentation techniques so our paper is very useful one in the today world.

Keywords

Genetic Algorithms, Human Character Segmentation, K-Means Algorithm, Living or Non-Living Things Segmentation, Video Object Segmentation.

References

[1] B. Alexe, T. Deselaers, and V. Ferrari. What is an Object? In CVPR, 2010.
[2] Y. J. Lee and K. Grauman. Collect-Cut: Segmentation with Top-Down Cues Discovered in Multi-Object Images. In CVPR, 2010.
[3] W. Brendel and S. Todorovic. Video Object Segmentation by Tracking Regions. In ICCV, 2009.
[4] J. Carreira and C. Sminchisescu. Constrained Parametric Min-Cuts for Automatic Object Segmentation. In CVPR, 2010.
[5] I. Endres and D. Hoiem. Category Independent Object Proposals. In ECCV, 2010.
[6] J. Kim and K. Grauman. Boundary-Preserving Dense Local Regions. In CVPR, 2011.
[7] C. Liu. Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. PhD thesis, MIT, 2009.
[8] D. Gao, V.Mahadevan, and N. Vasconcelos. The Discriminant CenterSurround Hypothesis for Bottom-Up Saliency. In NIPS, 2007.
[9] G. Kim and A. Torralba. Unsupervised detection of regions of interest using iterative link analysis. In NIPS, 2009. [10] D. Liu and T. Chen. DISCOV: A Framework for Discovering Objects in Video. In MM, 2008.
[11] D. Tsai, M. Flagg, and J.M.Rehg. Motion Coherent Tracking with MultiLabel MRF Optimization. In BMVC, 2010.
[12] A. Vazquez-Reina, S. Avidan, H. Pfister, and E. Miller. Multiple Hypothesis Video Segmentation from Superpixel Flows. In ECCV, 2010.
[13] J. Shi and J. Malik. Motion Segmentation and Tracking Using Normalized Cuts. In ICCV, 1998.
[14] Z. Yin and R. Collins. Shape Constrained Figure-Ground Segmentation and Tracking. In CVPR, 2009.
[15] J. Yuen, B. Russell, C. Liu, and A. Torralba. Labelme Video: Building a Video Database with Human Annotations. In ICCV, 2009.
[16] B. Price, B. Morse, and S. Cohen. Livecut: Learning-based Interactive Video Segmentation by Evaluation of Multiple Propagated Cues. In ICCV, 2009.
[17] Falkanauer, E. Genetic Algorithms and Grouping Problems. Wiley, 1998.
[18] Poli, R. Genetic programming for image analysis. Proc. Genetic Programming 1996. MIT Press, pp. 363-368.
[19] M. Gelgon and P. Bouthemy. A region-level motion-based graph representation and labelling for tracking a spatial image partition. In EMMCVPR, 1997.
[20] F. Moscheni, S. Bhattacharjee, and M. Kunt. Spatiotemporal segmentation based on region merging. IEEE TPAMI, 20(9):897–915, 1998. 
[21] J. Shi and J. Malik. Motion segmentation and tracking using normalized cuts. In ICCV, pages 1154–1160, Jan 1998. 
[22] J.Wang, B. Thiesson, Y. Xu, and M. Cohen. Image and video segmentation by anisotropic kernel mean shift. In ECCV, 2004. 
[23] C. Lettera and L. Mastera. Foreground/background segmentation in videotelephony. Signal Processing: Image Communication, 1:181{189, 1991.
[24] P. Gerken, M. Wollborn, and S. Schultz. An object-based layered codec as proposal for MPEG-4 scalability and compression tests - technical description. ISO/IEC JTC1/SC29/WG11, MPEG 95/359, 1995.
[25] S. Colonnese, A. Neri, G. Russo, and P. Talone. Moving objects versus still background classi_cation: a spatial temporal segmentation tool for MPEG-4. ISO/IEC JTC1/SC29/WG11, MPEG 96/571, 1996.
[26] T. Aach, A. Kaup, and R. Mester. Statistical model-based change detection in moving video. Signal Processing, 31:165{180, 1993.