VISUAL ATTENTION MODEL WITH ADAPTIVE WEIGHTING OF CONSPICUITY MAPS FOR BUILDING DETECTION IN SATELLITE IMAGES

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

4
Reader(s)
22
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 5 , ISSUE 4 (December 2012) > List of articles

VISUAL ATTENTION MODEL WITH ADAPTIVE WEIGHTING OF CONSPICUITY MAPS FOR BUILDING DETECTION IN SATELLITE IMAGES

A.-M. Cretu * / P. Payeur *

Keywords : Visual attention, weighting schemes, neural networks, building detection, satellite images.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 5, Issue 4, Pages 742-766, DOI: https://doi.org/10.21307/ijssis-2017-505

License : (CC BY-NC-ND 4.0)

Received Date : 24-August-2012 / Accepted: 05-November-2012 / Published Online: 01-December-2012

ARTICLE

ABSTRACT

The lack of automation and the limited performance of current image processing techniques pose critical challenges to the efficient and timely use of the large amount of data made available by aerial and space based assets. The imitation of fast adaptation and inference capability of human visual system appears to be a promising research direction for the development of computational algorithms able to deal with large variations in image content, characteristics and scale as those encountered in satellite imaging. The paper explores the potential use of an improved computational model of visual attention for the complex task of building identification in satellite images. It contributes to extend the envelope of application areas of such models and also to expand their current use from single object to multiple object detection. A set of original weighting schemes based on the contribution of different features to the identification of building and non-building areas is first proposed and evaluated against existing solutions in the literature. A novel adaptive algorithm then chooses the best weighting scheme based on a similarity error to ensure the best performance of the attention model in a given context. Finally, a neural network is trained to predict the set of weights provided by the best weighting scheme for the context of the image in which buildings are to be detected. The solution provides encouraging results on a set of 50 satellite images.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

[1] P. Wide, “Human-Based Sensing – Sensor Systems to Complement Human Perception”, Int. Journal Smart Sensing and Intelligent Systems, vol. 1, no.1, pp. 57 – 69, 2008.
[2] D. Walther, L. Itti, M. Riesenhuber, T. Poggio, and C. Koch, “Attentional Selection for Object Recognition – A Gentle Way”, Int. Workshop Biologically-Motivated Computer Vision, LNCS 2525, pp. 472 – 479, Springer, 2002.
[3] V. Gopalakrishnan, Y. Hu, and D. Rajan, “Salient Region Detection by Modeling Distributions of Color and Orientation”, IEEE Trans. Multimedia, vol. 11, no. 5, pp. 892 – 905, 2009.
[4] H. Kim and W. Kim, “Salient Region Detection Using Discriminative Feature Selection”, Advanced Concepts for Intelligent Vision Systems, J. Blanc-Talon et al. (Eds.): LNCS 6915, pp. 305 – 315, Springer, 2011.
[5] Y.F. Ma and H.J. Zhang, “Contrast-Based Image Attention Analysis by Using Fuzzy Growing”, Int. Conf. Multimedia, vol. 1, pp. 374 – 381, 2003.
[6] L. Itti and C. Koch, “Feature Combination Strategies for Saliency-Based Visual Attention Systems”, Electronic Imaging, vol. 10, no. 1, pp. 161 – 169, 2001.
[7] C. Zhao and C. Liu, “Sparse Embedding Feature Combination Strategy for Saliency-Based Visual Attention System”, Journal of Comp. Inf. Syst., vol. 6, no. 9, pp. 2831 – 2838, 2010.
[8] Y. Hu, X. Xie, W.-Y. Ma, L.-T. Chia, and D. Rajan, “Salient Region Detection Using Weighted Feature Maps Based on the Visual Attention Model”, Advances in Multimedia
Information Processing, LNCS 3332, pp. 993 – 1000, 2004.
[9] Y. Hu, D. Rajan, and L.-T. Chia, “Adaptive Local Context Suppression of Multiple Cues for Salient Visual Attention Detection”, Int. Conf. Multimedia and Expo, pp. 1 – 4, 2005.
[10] V. Gopalakrishnan, Y. Hu, and D. Rajan, “Unsupervised Feature Selection for Salient Object Detection”, Asian Conference on Computer Vision, R. Kimmel, and A. Sugimoto (Eds.): LNCS 6493, pp. 15 – 26, 2011.
[11] C.T. Vu and D.M. Chandler, “Main Subject Detection Via Adaptive Feature Selection”, Int. Conf. Image Processing, pp. 3101 – 3104, Cairo, 2009.
[12] S. Frintrop, “VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search,” Ph.D. Thesis, Germany, 2006.
[13] S. Goferman, L. Zelnik-Manor, and A. Tal, “Context-Aware Saliency Detection“, IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2376 – 2383, 2010.
[14] N. Murray, M. Vanrell, X. Otazu, and A. Parraga, “Saliency Estimation Using a Non-Parametric Low-Level Vision Model”, IEEE Conf. on Computer Vision and Pattern Recognition, pp. 433 – 440, 2011.
[15] R. Achanta and S. Susstrunk, “Saliency Detection Using Maximum Symmetric Surround”, Int. Conf. on Image Processing, pp. 2653 – 2656, Hong Kong, 2010.
[16] R. Achanta, S. Hemami, F. Estrada and S. Susstrunk, “ Frequency-Tuned Salient Region Detection”, IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1597 – 1604, 2009.
[17] X. Hou and L. Zhang, “Saliency Detection: A Spectral Residual Approach”, IEEE Conf.
on Computer Vision and Pattern Recognition, pp. 17 – 22, USA, 2007.
[18] X. Hou and L. Zhang, “Dynamic Visual Attention: Searching for Coding Length
Increments”, Conf. Neural Information Processing Systems, pp. 681 – 688, 2008.
[19] Y. Zhai and M. Shah, “Visual Attention Detection in Video Sequences Using
Spatiotemporal Cues”, ACM Multimedia, pp. 815 – 824, 2006.
[20] M.-M. Cheng, G.-X. Zhang, N. J. Mitra, X. Huang, and S.-M. Hu, “Global Contrast Based
Salient Region Detection”, IEEE Conf. on Computer Vision and Pattern Recognition, pp. 409 –
416, 2011.
[21] L. Zhang, M. H. Tong, T.K. Marks, H. Shan, and G. W. Cotrell, “SUN: A Bayesian
Framework for Saliency Using Natural Statistics”, Journal of Vision, vol. 8, no. 7, pp. 1 – 20,
2008.
[22] A.-M. Cretu and P. Payeur, “Biologically-Inspired Visual Attention Features for a Vehicle
Classification Task”, Int. Journal Smart Sensing and Intelligent Systems, vol. 4, no. 3, pp. 402 –
423, 2011.
[23] P.K. Kaiser and R.M. Boynton, Human Color Vision, Washington DC, Optical Society of
America, 1996.
[24] R.C. Gonzalez, R.E. Woods, and S.L. Eddins, Digital Image Processing Using Matlab,
Upper Saddle River, NJ, Prentice Hall, 2004.
[25] N. Shorter and T. Kasparis, “Automatic Vegetation Identification and Building Detection
from a Single Nadir Aerial Image”, Remote Sensing Journal, vol. 1, pp. 731 – 757, 2009.
[26] M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design, PWS Publishing
Co., 1996.
[27] www.mapquest.com.

EXTRA FILES

COMMENTS