Implementing Outsourcing for Project Management in Information Technology

Authors

  • Muhammad Awais Niaz Department of Computer Sciences University of Wah,Wah Cantt
  • Ahmed Saeed Department of Computer Sciences University of Wah,Wah Cantt
  • Husnain Ahmed Department of Computer Sciences University of Wah,Wah Cantt

Keywords:

Salient object, automated detection, background removal, graph cut.

Abstract

Automatic salient object detection is the process of segment the sali-ent object or useful
information from input images without any pre-vious knowledge and assumption of the content
of the corresponding images scene. It is used in many computer vision and computer graphics
application. Detecting salient object with complex back-ground and biased dataset is difficult
and challenging problem. In this manuscript, we proposed an effective and efficient supervised
method based on graph cuts to detect the salient object. The pro-posed method performs the
segmentation process directly on the in-put image without first converting it into binary form.
Therefore, the segmented salient object contains the same color space as the origi-nal input
image. The proposed method is tested on MSRA and DUT-OMRON benchmark datasets and
it performs better compared to existing methods.

References

Z. Liu, R. Shi, L. Shen, Y. Xue, K. N. Ngan, and Z. Zhang, Unsupervised salient object segmentation based on

kernel density estimation and two phase graph cut, IEEE Trans. Multimedia, vol. 14, no. 4, pp. 12751289, Aug.

Y.-S. Wang, C.-L. Tai, O. Sorkine, and T.-Y. Lee, Optimized scale and stretch for image resizing, ACM Trans.

Graph., vol. 27, no. 5, p. 118, Dec. 2008

V. Navalpakkam and L. Itti, An integrated model of top-down and bottom-up attention for optimizing detection

speed, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2006, pp. 20492056.

A. Ninassi, O. Le Meur, P. Le Callet, and D. Barbba, Does where you gaze on an image affect your perception

of quality? Applying visual attention to image quality metric, in Proc. IEEE Int. Conf. Image Process. (ICIP), vol.

Sep./Oct. 2007, pp. II-169II-172

J. Park, J.-Y. Lee, Y.-W. Tai, and I. S. Kweon, Modeling photo composition and its application to photo rearrangement, in Proc. IEEE Int. Conf. Image Process. (ICIP), Sep./Oct. 2012, pp. 27412744.

L. Marchesotti, C. Cifarelli, and G. Csurka, A framework for visual saliency detection with applications to

image thumbnailing, in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Sep./Oct. 2009, pp. 22322239.

L. Itti, Automatic foveation for video compression using a neurobiological model of visual attention, IEEE

Trans. Image Process., vol. 13, no. 10, pp. 13041318, Oct. 2004

J. Feng, Y. Wei, L. Tao, C. Zhang, and J. Sun, Salient object detection by composition, in Proc. IEEE Int. Conf.

Comput. Vis. (ICCV), Nov. 2011, pp. 10281035

D. A. Klein, D. Schulz, and A. B. Cremers. Realtime hierarchical clustering based on boundary and surface

statistics. In Asian Conf. on Computer Vision (ACCV), 2016

Klein DA, Illing B, Gaspers B, Schulz D, Cremers AB. Hierarchical Salient Object Detection for Assisted

Grasping. arXiv preprint arXiv:1701.04284. 2017 Jan 16.

Aytekin Ç, Iosifidis A, Kiranyaz S, Gabbouj M. Learning graph affinities for spectral graph-based salient

object detection. Pattern Recognition. 2017 Apr 30;64:159-67.

Yang B, Zhang X, Chen L, Yang H, Gao Z. Edge guided salient object detection. Neurocomputing. 2017 Jan

;221:60-71.

Aksac A, Ozyer T, Alhajj R. Complex Networks Driven Salient Region Detection based on Superpixel

Segmentation. Pattern Recognition. 2017 Jan 8.

Peng H, Li B, Ling H, Hu W, Xiong W, Maybank SJ. Salient object detection via structured matrix

decomposition. IEEE transactions on pattern analysis and machine intelligence. 2016 May 4.

Singh, N., Arya, R. and Agrawal, R.K., 2016. A novel position prior using fusion of rule of thirds and image

center for salient object detection. Multimedia Tools and Applications, pp.1-18.

Arya R, Singh N, Agrawal RK. A novel hybrid approach for salient object detection using local and global

saliency in frequency domain. Multimedia Tools and Applications. 2016 Jul 1;75(14):8267-87

Arya R, Singh N, Agrawal RK. A novel combination of second-order statistical features and segmentation

using multi-layer superpixels for salient object detection. Applied Intelligence. 2016:1-8

Lei J, Wang B, Fang Y, Lin W, Le Callet P, Ling N, Hou C. A universal framework for salient object detection.

IEEE Transactions on Multimedia. 2016 Sep;18(9):1783-95.

Klein DA, Frintrop S. Center-surround divergence of feature statistics for salient object detection. InComputer

Vision (ICCV), 2011 IEEE International Conference on 2011 Nov 6 (pp. 2214-2219). IEEE.

Zhang J, Ehinger KA, Wei H, Zhang K, Yang J. A novel graph-based optimization framework for salient

object detection. Pattern Recognition. 2017 Apr 30;64:39-50.

Cheng, Ming-Ming, Niloy J. Mitra, Xiaolei Huang, Philip HS Torr, and Shi-Min Hu. "Global contrast based

salient region detection." IEEE Transactions on Pattern Analysis and Machine Intelligence 37, no. 3 (2015): 569-

H. Jiang, J. Wang, Z. Yuan, T. Liu, N. Zheng, and S. Li, “Automatic salient object segmentation based on

context and shape prior,” in Proceedings of British Machine Vision Conference, 2011

Y.-S. Wang, C.-L. Tai, O. Sorkine, and T.-Y. Lee, Optimized scale and stretch for image resizing, ACM

Trans. Graph., vol. 27, no. 5, p. 118, Dec. 2008

Kim J, Han D, Tai YW, Kim J. Salient Region Detection via High-Dimensional Color Transform and Local

Spatial Support. Image Processing, IEEE Transactions on. 2016 Jan;25(1):9-23.

N. Xu, N. Ahuja, and R. Bansal, “Object segmentation using graph cuts based active contours,” Computer

Vision and Image Understanding, vol. 107, no. 3, pp. 210-224, 2007.

Y.-S. Wang, C.-L. Tai, O. Sorkine, and T.-Y. Lee, Optimized scale and stretch for image resizing, ACM

Trans. Graph., vol. 27, no. 5, p. 118, Dec. 2008

Kim J, Han D, Tai YW, Kim J. Salient Region Detection via High-Dimensional Color Transform and Local

Spatial Support. Image Processing, IEEE Transactions on. 2016 Jan;25(1):9-23.

Cheng, J. Warrell, W.-Y. Lin, S. Zheng, V. Vineet, and N. Crook, “Efficient salient region detection with soft

image abstraction,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Dec. 2013, pp. 1529–1536.

Houwen Peng, Bing Li, Rongrong Ji,Weiming Hu,Weihua Xiong, Congyan Lang,“Salient Object Detection

via Low-Rank and Structured Sparse Matrix Decomposition”, Proceedings of the Twenty-Seventh AAAI

Conference on Artificial Intelligence,2013

H. Jiang, J. Wang, Z. Yuan, Y. Wu, N. Zheng, and S. Li, Salient object detection: A discriminative regional

feature integration approach, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2013, pp.

Published

2019-11-01

How to Cite

Muhammad Awais Niaz, Ahmed Saeed, & Husnain Ahmed. (2019). Implementing Outsourcing for Project Management in Information Technology. University of Wah Journal of Computer Science, 2(1). Retrieved from https://uwjcs.org.pk/index.php/ojs/article/view/13