Journal of Shanghai Jiaotong University(Science) >
Exploiting Local and Global Characteristics for Contrast Based Visual Saliency Detection
Online published: 2015-03-10
Visual saliency is an important cue in human visual system to identify salient region in the image; it can be useful in many applications including image retrieval, object recognition, image segmentation, etc. Image contrast has been used as an effective feature to detect visual salient region. However, the conventional contrast measures either in spectral domain or in spatial domain fail to give sufficient consideration towards the local and global characteristics of the image. This paper presents a visual saliency detection algorithm based on a novel contrast measurement. This measurement extracts the spectral information of image block using the 2D discrete Fourier transform (DFT), and combines with the total variation (TV) of image block in spatial domain. The proposed algorithm is used to perform salient region detection in the image, and compared with state-of-the-art algorithms. The experimental results from the MSRA dataset validate the effectiveness of the proposed algorithm.
Key words： visual saliency; contrast measure; multi-scale; local contrast; global contrast
XU Xin1,2* (徐 新), WANG Ying-lin3 (王英林), ZHANG Xiao-long1,2 (张晓龙) . Exploiting Local and Global Characteristics for Contrast Based Visual Saliency Detection[J]. Journal of Shanghai Jiaotong University(Science), 2015 , 20(1) : 14 -20 . DOI: 10.1007/s12204-015-1581-3
[1] Chen L Q, Xie X, Fan X, et al. A visual attention model for adapting images on small displays [J]. Multimedia Systems, 2003, 9(4): 353-364.
[2] Santella A, Agrawala M, Decarlo D, et al.Gazebased interaction for semi-automatic photo cropping [C]// Proceedings of SIGCHI Conference on Human Factors in Computing Systems. [s.l.]: ACM, 2006: 771-780.
[3] Gopalakrishnan V, Hu Y Q, Rajan D. Random walks on graphs to model saliency in images [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [s.l.]: IEEE, 2009: 1698-1705.
[4] Cheng M M, Zhang G X, Mitra N J, et al. Global
contrast based salient region detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [s.l.]: IEEE, 2011: 409-416.
[5] Hou X, Zhang L Q. Dynamic visual attention:Searching for coding length increments [J]. Advances in Neural Information Processing Systems, 2008, 21:681-688.
[6] Gopalakrishnan V, Hu Y Q, Rajan D. Salient region detection by modeling distributions of color and orientation [J]. IEEE Transactions on Multimedia,2009, 11(5): 892-905.
[7] Aziz M Z, Mertsching B. Fast and robust generation of feature maps for region-based visual attention [J].IEEE Transactions on Image Processing, 2008, 17(5):633-644.
[8] Felzenszwalb P F, Girshick R B, Mcallester D,et al. Object detection with discriminatively trained part based models [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645.
[9] Cerf M, Harel J, Einh¨aeuser W, et al. Predicting human gaze using low-level saliency combined with face detection [J]. Advances in Neural Information Processing Systems, 2008, 21: 241-248.
[10] Kootstra G, Nederveen A, Boer B D. Paying attention to symmetry [C]// Proceedings of the British Machine Vision Conference. Leeds, UK: British Machine Vision Association, 2008: 1115-1125.
[11] Treisman A M, Gelade G. A feature-integration theory of attention [J]. Cognitive Psychology, 1980,12(1): 97-136.
[12] Koch C, Ullman S. Shifts in selective visual attention:Towards the underlying neural circuitry [J]. Human Neurobiology, 1985, 4(4): 219-227.
[13] Itti L, Koch C, Niebur E. A model of saliency based visual attention for rapid scene analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998, 20(11): 1254-1259.
[14] Lu Y, Zhang W, Lu H, et al. Salient object detection using concavity context [C]// Proceedings of the IEEE International Conference on Computer Vision. [s.l.]:IEEE, 2011: 233-240.
[15] Shi K, Wang K, Lu J, et al. PISA: Pixelwise image saliency by aggregating complementary appearance contrast measures with spatial priors [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [s.l.]: IEEE, 2013: 2115-2122.
[16] Goferman S, Zelnik-Manor L, Tal A. Contextaware saliency detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011,34(10): 1915-1926.
[17] Achanta R, Hemami S, Estrada F, et al.Frequency-tuned salient region detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [s.l.]: IEEE, 2009: 1597-1604.
[18] Hou X, Zhang L Q. Saliency detection: A spectral residual approach [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.[s.l.]: IEEE, 2007: 1-8.
[19] Vu C T, Phan T D, Chandler D M. S3: A spectral and spatial measure of local perceived sharpness in natural images [J]. IEEE Transactions on Image Processing,2012, 21(3): 934-945.
[20] Li Chong-fei, Gao Ying-hui, Lu Kai, et al. Saliency detection method based on phase spectrum and amplitude spectrum tuning [J]. Journal of Image and Graphics,2012, 17(7): 821-827 (in Chinese).
[21] Liu T, Sun J, Zheng N, et al. Learning to detect a salient object [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(2): 353-367.
[22] Harel J, Koch C, Perona P. Graph-based visual saliency [J]. Advances in Neural Information Processing Systems, 2007, 20: 545-552.
[23] Zhang L, Marks T K, Tong M H, et al. SUN: A Bayesian framework for saliency using natural statistics[J]. Journal of Vision, 2008, 8(7): 1-20.
[24] Murray N, VanrellM, Otazu X, et al. Saliency estimation using a non-parametric low-level vision model [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. [s.l.]: IEEE, 2011:433-440.
/
| 〈 |
|
〉 |
