Saliency-constrained semantic learning for airport target recognition of aerial images

Journal of Applied Remote Sensing

Volumn 9, Issue 1, 2015, Pages

  Zhao, Danpei ^a,b   Shi, Jun ^a,b   Wang, Jiajia ^a,b   Jiang, Zhiguo ^a,b  

^a BEIHANG UNIVERSITY   (China)

^b Beijing Key Laboratory of Digital Media   (China)

Author keywords

bag of features; locality preserving indexing; object recognition; saliency detection; visual attention model

Indexed keywords

AIRPORTS; ANTENNAS; BEHAVIORAL RESEARCH; FEATURE EXTRACTION; INDEXING (OF INFORMATION); K-MEANS CLUSTERING; OBJECT RECOGNITION; REMOTE SENSING;

AUTOMATIC RECOGNITION; BAG OF FEATURES; HIGH LEVEL SEMANTICS; LOCALITY PRESERVING INDEXING; REMOTE SENSING IMAGES; SALIENCY DETECTION; SCALE INVARIANT FEATURE TRANSFORMS; VISUAL ATTENTION MODEL;

IMAGE ENHANCEMENT;

EID: 84929095406     PISSN: None     EISSN: 19313195     Source Type: Journal    
DOI: 10.1117/1.JRS.9.096058     Document Type: Article

References (21)

1. F. T. Mahmoudi, F. Samadzadegan, and P. Reinartz, "Object recognition based on the context aware decision-level fusion in multiviews imagery," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8(1), 12-22 (2014).
2. A.-M. Cretu and P. Payeur, "Building detection in aerial images based on watershed and visual attention feature descriptors," in 2013 Int. Conf. on Computer and Robot Vision (CRV), pp. 265-272 (2013).
3. A. M. Cheriyadat, "Unsupervised feature learning for aerial scene classification," IEEE Trans. Geosci. Remote Sens. 52(1), 439-451 (2014).
4. P. L. Zhang, Z. Y. Lv, and W. Z. Shi, "Object-based spatial feature for classification of very high resolution remote sensing images," IEEE Geosci. Remote Sens. Lett. 10(6), 1572-1576 (2013).
5. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vision 60(2), 91-110 (2004).
6. H. Bay et al., "SURF: speeded up robust features," Comput. Vision Image Understanding 110(3), 346-359 (2008).
7. E. Rosten and T. Drummond, "Machine learning for high-speed corner detection," in Proc. European Conf. on Computer Vision (ECCV), pp. 430-443 (2006).
8. M. Calonder et al., "Brief: binary robust independent elementary features," in Proc. European Conf. on Computer Vision (ECCV), pp. 778-792 (2010).
9. S. Leutenegger, M. Chli, and R. Siegwart, "BRISK: binary robust invariant scalable keypoints," in 2011 IEEE Int. Conf. on Computer Vision (ICCV), pp. 2548-2555 (2011).
10. E. Rublee et al., "ORB: an efficient alternative to SIFT or SURF," in 2011 IEEE Int. Conf. on Computer Vision (ICCV), pp. 2564-2571 (2011).
11. X. Hou and L. Zhang, "Saliency detection: a spectral residual approach," in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 1-8 (2007).
12. S. Goferman, L. Zelnik-Manor, and A. Tal, "Context-aware saliency detection," in Proc. Int. Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2376-2383 (2010).
13. N. H. Dardas and N. D. Georganas, "Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques," IEEE Trans. Instrum. Meas. 60(11), 3592-3607 (2011).
14. L. Wang et al., "Exploiting feature correspondence constraints for image recognition," in 2011 18th IEEE Int. Conf. on Image Processing (ICIP), pp. 1769-1772 (2011).
15. A. Y. Ng, M. Jordan, and Y.Weiss, "On spectral clustering: analysis and an algorithm," Adv. Neural Inf. Proc. Syst. 14, 849-856 (2001).
16. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888-905 (2000).
17. D. Cai, X. He, and J. Han, "Document clustering using locality preserving indexing," IEEE Trans. Knowl. Data Eng. 17(12), 1624-1637 (2005).
18. L. Fei-Fei and P. Perona, "A Bayesian hierarchical model for learning natural scene categories," in IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 524-531 (2005).
19. J. Wang et al., "Locality-constrained linear coding for image classification," in 2010 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 3360-3367 (2010).
20. Y. Zhai and M. Shah, "Visual attention detection in video sequences using spatiotemporal cues," in Proc. of ACM Multimedia, pp. 815-824 (2006).
21. The UCI machine learning repository http://archive.ics.uci.edu/ml/about.html.