Browse > Article
http://dx.doi.org/10.3837/tiis.2018.02.008

Visual Saliency Detection Based on color Frequency Features under Bayesian framework  

Ayoub, Naeem (Department of Computer science and technology, Dalian University of technology)
Gao, Zhenguo (Department of Computer science and technology, Dalian University of technology)
Chen, Danjie (College of software, Beijing institute of technology)
Tobji, Rachida (Department of Computer science and technology, Dalian University of technology)
Yao, Nianmin (Department of Computer science and technology, Dalian University of technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.12, no.2, 2018 , pp. 676-692 More about this Journal
Abstract
Saliency detection in neurobiology is a vehement research during the last few years, several cognitive and interactive systems are designed to simulate saliency model (an attentional mechanism, which focuses on the worthiest part in the image). In this paper, a bottom up saliency detection model is proposed by taking into account the color and luminance frequency features of RGB, CIE $L^*a^*b^*$ color space of the image. We employ low-level features of image and apply band pass filter to estimate and highlight salient region. We compute the likelihood probability by applying Bayesian framework at pixels. Experiments on two publically available datasets (MSRA and SED2) show that our saliency model performs better as compared to the ten state of the art algorithms by achieving higher precision, better recall and F-Measure.
Keywords
Saliency Detection; image processing; vision system; Bayesian Saliency; Color frequency; Log-Gabor filter;
Citations & Related Records
연도 인용수 순위
  • Reference
1 H. J. Seo, P. Milanfar, "Static and space-time visual saliency detection by self-resemblance," Journal of Vision, vol. 9, no.12, pp.1-27, November, 2009.
2 T. N. Vikram, M. Tscherepanow and B. Wrede, "A random center surround bottom up visual attention model useful for salient region detection," in Proc. of Proceedings of the IEEE Workshop on Applications of Computer Vision, Kona, HL, USA, pp. 166-173, February, 2011.
3 W. H. Tsai, "Moment-preserving thresholding: a new approach, Computer Vision," Graphics and Image Processing, vol. 29, no. 3, pp. 377-393, March, 1985.   DOI
4 R. B. S. Alpert, M. Galun, and A. Brandt, "Image segmentation by probabilistic bottom-up aggregation and cue integration," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, July, 2007.
5 L. Itti, C. Koch and E., Niebur, "A model of saliency based visual attention for rapid scene analysis," IEEE Transactions on Pattern analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254-1259, November, 1998.   DOI
6 A. M. Treisman and G. Gelade, "A feature-integration theory of attention," Cognitive Psychology, vol. 12, no. 1, pp. 97-136, January, 1980.   DOI
7 M. Jian, K. M. Lam and J. Dong, "Facial-feature detection and localization based on a hierarchical scheme," Information Sciences, vol. 262, pp. 1 - 14, March, 2014.   DOI
8 M. W. Jian, J. Y. Dong and J. Ma, "Image retrieval using wavelet-based salient regions," The Imaging Science Journal, vol. 59, no. 4, pp. 219-231, 2011.   DOI
9 P. Khuwuthyakorn, A Robles-Kelly and J. Zhou, "Object of interest detection by saliency learning," in Proc. of Proceedings of the European Conference on Computer Vision, Heraklion, Crete, Greece, pp. 636-649, September, 2010.
10 L. Shi, J. Wang, L. Xu, H. Lu and C. Xu, "Context saliency based image summarization," in Proc. of Proceedings of the IEEE International Conference on Multimedia and Expo, New York, USA, pp. 270-273, July, 2009.
11 T. Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang and X. Y. Shum, "Learning to detect a salient object," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 2, pp. 353-367, February, 2011.   DOI
12 M. Donoser, M. Urschler, M. Hirzer and H. Bischof, "Saliency driven total variation segmentation," in Proc. of Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan, pp. 817-824, September, 2009.
13 R. Achanta, F. Estrada, P. Wils and S. Susstrunk, "Salient region detection and segmentation," in Proc. of Proceedings of the International Conference on Computer Vision Systems, Santorini, Greece, pp. 66-75, May, 2008.
14 C. Guo, L. Zhang, "A Novel Multiresolution Spatiotemporal Saliency Detection Model and Applications in Image and Video Compression," IEEE Transactions on Image Processing, vol. 19, no. 1, pp. 185-198, January, 2010.   DOI
15 Y. Nagai, "From bottom-up visual attention to robot action learning," in Proc. of Proceedings of the IEEE International Conference on Development and Learning, Shanghai, China, pp. 1-6, June, 2009.
16 J. Zhang, , M. Wang, S. Zhang, X. Li and X. Wu, "Spatiochromatic Context Modeling for Color Saliency Analysis," IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 6, pp. 1177-1189, June, 2016.   DOI
17 M. Jian, K. M. Lam, J. Dong and L. Shen, "Visual-Patch-Attention-Aware Saliency Detection." IEEE Transactions on Cybernetics, vol. 45, no. 8, pp. 1575 - 1586, August, 2015.   DOI
18 M. Cheng, N.J. Mitra, X. Huang, P. H. S. Torr and S. H. Hu, "Global contrast based salient region detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 3, pp. 569 - 582, March, 2015.   DOI
19 R. Achanta, S. Hemami, F. Estrada and S. Susstrunk, "Frequency tuned salient region detection," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, pp. 1597- 1604, June, 2009.
20 J. Harel, C. Koch and P. Perona, "Graph-based visual saliency," Advances in Neural Information Processing Systems, pp. 545- 552, December, 2007.
21 M. Jian, Q. Qi, J. Dong, S. Sun and K.M. Lam, "Saliency detection using quaternionic distance based weber descriptor and object cues," in Proc. of Signal and Information Processing Association Annual Summit and Conference (APSIPA), Asia-Pacific, jeju, south Korea, December 13-16, 2016.
22 V. Setlur, T. Lechner, M. Nienhaus and B. Gooch, "Retargeting Images and Video for Preserving Information Saliency," IEEE Computer Graphics and Applications, vol. 27, no. 5, pp. 80 - 88, September, 2007.   DOI
23 D. J. Field, "Relations between the statistics of natural images and the response properties of cortical cells," Journal of the Optical Society of America. A, vol. 4, no.12, pp. 2379-2394, December, 1987.   DOI
24 M. Guttmann, L. Wolf and C. O. Danny, "Content aware video manipulation," Computer Vision and Image Understanding, vol. 115, no. 12, pp. 1662-1678, December, 2011.   DOI
25 B. C. Ko and J. Y. Nam, "Object-of-interest image segmentation based on human attention and semantic region clustering," Journal of the Optical Society of America A, vol. 23, no. 10, pp. 2462-2470, 2006.   DOI
26 J. Han, K.N. Ngan, M. Li and H. J. Zhang, "Unsupervised extraction of visual attention objects in color images," IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no. 1, pp. 141-145, January, 2006.   DOI
27 U. Rutishauser, D. Walther, C. Koch and P. Perona, "Is bottom-up attention useful for object recognition?" in Proc. of Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04), Washington, DC, USA, July, 2004.
28 J. S. Kim, J. H. Kim and C. S. Kim, "Adaptive image and video retargeting technique based on fourier analysis," in Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR'09), Miami, FL, USA, pp. 1730-1737, June, 2009.
29 J. V. D. Weijer, T. Gevers and A. D. Bagdanov, "Boosting color saliency in image feature detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 1, pp. 150-156. January, 2006.   DOI
30 C. Harris and M. Stephens, "A combined corner and edge detector," in Proc. of Fourth Alvey Vision Conference, pp. 147-151, 1988.
31 P. Kovesi, "Image features from phase congruency," Videre: Journal of Computer Vision Research, vol. 1, pp. 1-30, June, 1995.
32 R. Achanta and S. Susstrunk, "Saliency detection using maximum symmetric surround," in Proc. of Proceedings of IEEE International Conference on Image Processing, Hong Kong, pp. 2653-2656, September, 2010.
33 C. Koch and S. Ullman, "Shifts in selective visual attention: towards the underlying neural circuitry," Human Neurobiology, vol. 4, no.4, pp. 219-227, 1985.
34 E. Rahtu, J. Kannala, M. Salo and J. Heikkila, "Segmenting salient objects from images and videos," in Proc. of Proceedings of European Conference on Computer Vision (ECCV'10), Crete, Greece, pp. 366-379, September, 2010.
35 Y. Xie, H. Lu and M. H. Yang, "Bayesian saliency via low and mid level cues," IEEE Transactions on Image Processing, vol. 22, no. 5, pp. 1689 -1698, May, 2013.   DOI
36 L. Zhang, M. H. Tong, T. K. Marks, H. Shan and G. W. Cottrell, "SUN: A Bayesian framework for saliency using natural statistics," Journal of Vision, vol. 8, no. 7, pp. 1-20, December, 2008.
37 T. N. Vikram, M. Tscherepanow and B. Wrede, "A saliency map based on sampling an image into random rectangular regions of interest," Pattern Recognition, vol. 45, no. 9, pp. 3114-3124, September, 2012.   DOI