A Novel Salient Region Detection Method Based on Hierarchical Spatial Information

LIU Shuo; DING Wenrui; LI Hongguang; LI Yingting

doi:10.1049/cje.2017.01.027

Volume 26 Issue 2

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2017 > 26(2): 319-324

LIU Shuo, DING Wenrui, LI Hongguang, et al., “A Novel Salient Region Detection Method Based on Hierarchical Spatial Information,” Chinese Journal of Electronics, vol. 26, no. 2, pp. 319-324, 2017, doi: 10.1049/cje.2017.01.027

Citation:

LIU Shuo, DING Wenrui, LI Hongguang, et al., “A Novel Salient Region Detection Method Based on Hierarchical Spatial Information,” Chinese Journal of Electronics, vol. 26, no. 2, pp. 319-324, 2017, doi: 10.1049/cje.2017.01.027

Citation:

PDF( 1039 KB)

A Novel Salient Region Detection Method Based on Hierarchical Spatial Information

doi: 10.1049/cje.2017.01.027

1.
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China;
2.
Research Institute of Unmanned Aerial Vehicle, Beihang University, Beijing 100191, China;
3.
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61450008).

More Information

Corresponding author: DING Wenrui (corresponding author) received the B.S. degree in computer science from Beihang University in 1994. From 1994 to now, she was a lecturer of Beihang University where she is a now a professor, deputy general designer of UAV system. Her research interests include data link, computer vision, and artificial intelligence. (Email:ding@buaa.edu.cn)
Received Date: 2014-11-26
Rev Recd Date: 2015-05-05
Publish Date: 2017-03-10

Abstract

Abstract

Different patterns in one object will cause unequal saliency degree which makes it hard to highlight the object region uniformly. We propose a salient region detection method which mainly includes image abstraction, saliency calculation and integration. Under the detection framework, the hierarchical spatial information is introduced to improve the performance. The image abstraction with "pixel level" spatial information is applied to capture some meaningful elements. The local contrast is calculated with the "element level" spatial information. The "object level" spatial information is represented as compactness and background possibility, which further help to better pop out the object region and suppress the background. The results show that our method has a good performance even though the object consists of complex patterns.
- Salient region detection,
- Hierarchical spatial information,
- Compactness,
- Background possibility

FullText(HTML)

References(18)

References

C. Guo and L. Zhang, "A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression", IEEE Transactions on Image Processing, Vol.19, No.1, pp.185-198, 2010.

Y. Fu, J. Cheng, Z. Li et al., "Saliency cuts:An automatic approach to object segmentation", IEEE 19th International Conference on Pattern Recognition, Tampa, Florida, USA, pp.1-4, 2008.

D. Gao, S. Han and N. Vasconcelos, "Discriminant saliency, the detection of suspicious coincidences", and applications to visual recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.31, No.6, pp.989-1005, 2009.

Y. Fang, Z. Chen, W. Lin et al., "Saliency detection in the compressed domain for adaptive image retargeting", IEEE Transactions on Image Processing, Vol.21, No.9, pp.3888-3901, 2012.

Z. Chen, B. Zou, X. Gao et al., "Fusion visual attention and low-level features in images for region of interest extraction", Chinese Journal of Electronics, Vol.22, No.2, 2013.

L. Sui, J. Zhang, L. Zhuo et al., "Visual attention model based regions of interest detection in compressed domain", Chinese Journal of Electronics, Vol.21, No.4, pp.697-700, 2012.

T. Judd, K. Ehinger, F. Durand et al., "Learning to predict where humans look", IEEE 12th International Conference on Computer Vision, Kyoto, Japan, pp.2106-2113, 2009.

R. Achanta, S. Hemami, F. Estrada et al., "Frequency-tuned salient region detection", IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, USA, pp.1597-1604, 2009.

A. Borji and L. Itti, "State-of-the-art in visual attention modeling", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.35, No.1, pp.185-207, 2013.

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, 1998.

X. Hou and L. Zhang, "Saliency detection:A spectral residual approach", IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, Minnesota, USA, pp.1-8, 2007.

Y. Zhai and M. Shah, "Visual attention detection in video sequences using spatiotemporal cues", Proceedings of the 14th Annual ACM International Conference on Multimedia, New York, NY, USA, pp.1815-824, 2006.

M.M. Cheng, G.X. Zhang, N.J. Mitra et al., "Global contrast based salient region detection", IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, Colorado, USA, pp.409-416, 2011.

R. Achanta, A. Shaji, K. Smith et al., "SLIC superpixels compared to state-of-the-art superpixel methods", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.34, No.11, pp.2274-2282, 2012.

F. Perazzi, P. Krahenbuhl, Y. Pritch et al., "Saliency filters:Contrast based filtering for salient region detection", IEEE Conference on Computer Vision and Pattern Recognition, Providence, Rhode Island, USA, pp.733-740, 2012.

M.M. Cheng, J. Warrell, W.Y. Lin et al., "Efficient salient region detection with soft image abstraction", IEEE Conference on Computer Vision, Sydney, Australia, pp.1529-1536, 2013.

M.T. Orchard and C.A. Bouman, "Color quantization of images", IEEE Transactions on Signal Processing, Vol.39, No.12, pp.2677-2690, 1991.

R.C. Gonzalez and R.E. Woods, Digital Image Processing, Prentice Hall Press, 2002.

Relative Articles

Supplements(0)

Cited By

Proportional views