Local structure learning in high resolution remote sensing image retrieval

Neurocomputing

Volumn 207, Issue , 2016, Pages 813-822

  Du, Zhongxiang ^a,b   Li, Xuelong ^a   Lu, Xiaoqiang ^a  

^a XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

^b UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

High resolution remote sensing image retrieval; Lipschitz smooth; Manifold structure

Indexed keywords

IMAGE RECONSTRUCTION; LAND USE; MILITARY APPLICATIONS; MILITARY PHOTOGRAPHY; REMOTE SENSING; SPACE OPTICS; TRAINING AIRCRAFT;

HIGH RESOLUTION REMOTE SENSING IMAGES; LIPSCHITZ; MANIFOLD STRUCTURES; REMOTE SENSING IMAGES; STATE-OF-THE-ART APPROACH; STATISTICAL INFORMATION; STRUCTURAL INFORMATION; TOPOLOGICAL STRUCTURE;

IMAGE RETRIEVAL;

ARTICLE; DATA ANALYSIS; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE RETRIEVAL; KERNEL METHOD; LOCAL STRUCTURE LEARNING; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; REMOTE SENSING;

EID: 84977566735     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2016.05.061     Document Type: Article

References (38)

1. Yang, Y., Newsam, S., Geographic image retrieval using local invariant features. IEEE Trans. Geosci. Remote Sens. 51:2 (2013), 818–832.
2. D.G. Lowe, Object recognition from local scale-invariant features, in: 1999 The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, IEEE, Kerkyra, Greece, 1999, pp. 1150–1157.
3. Lowe, D.G., Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60:2 (2004), 91–110.
4. H. Bay, T. Tuytelaars, L. Van Gool, Surf: speeded up robust features, in: Computer Vision—ECCV 2006, Springer, Graz, Austria 2006, pp. 404–417.
5. J. Sivic, A. Zisserman, Video google: a text retrieval approach to object matching in videos, in: 2003 Proceedings of Ninth IEEE International Conference on Computer Vision, IEEE, Nice, France, 2003, pp. 1470–1477.
6. S. Lazebnik, C. Schmid, J. Ponce, Beyond bags of features: spatial pyramid matching for recognizing natural scene categories, in: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, IEEE, New York, USA, 2006, pp. 2169–2178.
7. G. Csurka, F. Perronnin, Fisher vectors: beyond bag-of-visual-words image representations, in: Computer Vision, Imaging and Computer Graphics, Theory and Applications, Springer, Berlin, Germany, 2011, pp. 28–42.
8. Jégou, H., Perronnin, F., Douze, M., Sánchez, J., Pérez, P., Schmid, C., Aggregating local image descriptors into compact codes. IEEE Trans. Pattern Anal. Mach. Intell. 34:9 (2012), 1704–1716.
9. R. Arandjelovic, A. Zisserman, All about vlad, in: 2013 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Portland, USA, 2013, pp. 1578–1585.
10. M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, in: NIPS, vol. 14, 2001, pp. 585–591.
11. J. Heinonen, Lectures on Lipschitz analysis, Univ., 2005.
12. T. Bretschneider, R. Cavet, O. Kao, Retrieval of remotely sensed imagery using spectral information content, in: 2002 IEEE International Geoscience and Remote Sensing Symposium, 2002, IGARSS'02, vol. 4, IEEE, Toronto, Canada, 2002, pp. 2253–2255.
13. Y. Hongyu, L. Bicheng, C. Wen, Remote sensing imagery retrieval based-on Gabor texture feature classification, in: 2004 Proceedings of 7th International Conference on Signal Processing, 2004, ICSP'04, vol. 1, IEEE, Beijing, China, 2004, pp. 733–736.
14. G.H. Joblove, D. Greenberg, Color spaces for computer graphics, in: ACM Siggraph Computer Graphics, vol. 12, ACM, Atlanta, USA, 1978, pp. 20–25.
15. T. Ojala, M. Pietikainen, D. Harwood, Performance evaluation of texture measures with classification based on Kullback discrimination of distributions, in: 1994 Proceedings of the 12th IAPR International Conference on Pattern Recognition, Conference A: Computer Vision & Image Processing, vol. 1, no. 1, 1994, pp. 582–585.
16. Wu, P., Manjunath, B., Newsam, S., Shin, H., A texture descriptor for browsing and similarity retrieval. Signal Process.: Image Commun. 16:1 (2000), 33–43.
17. E. Aptoula, Bag of morphological words for content-based geographical retrieval, in: 2014 12th International Workshop on Content-Based Multimedia Indexing (CBMI), IEEE, Klagenfurt, Austria, 2014, pp. 1–5.
18. Aptoula, E., Remote sensing image retrieval with global morphological texture descriptors. IEEE Trans. Geosci. Remote Sens. 52:5 (2014), 3023–3034.
19. Xu, S., Fang, T., Li, D., Wang, S., Object classification of aerial images with bag-of-visual words. IEEE Geosci. Remote Sens. Lett. 7:2 (2010), 366–370.
20. Jiang, F., Hu, H.-M., Zheng, J., Li, B., A hierarchal bow for image retrieval by enhancing feature salience. Neurocomputing 175 (2016), 146–154.
21. Liu, Z., Wang, S., Tian, Q., Fine-residual vlad for image retrieval. Neurocomputing 173 (2016), 1183–1191.
22. J. MacQueen, et al., Some methods for classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, California, USA, 1967, pp. 281–297.
23. Jaakkola, T., Haussler, D., et al. Exploiting generative models in discriminative classifiers. Adv. Neural Inf. Process. Syst., 1999, 487–493.
24. Dempster, A.P., Laird, N.M., Rubin, D.B., Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc. Ser. B, 1977, 1–38 (Methodological).
25. F. Perronnin, C. Dance, G. Csurka, M. Bressan, Adapted vocabularies for generic visual categorization, in: Computer Vision—ECCV 2006, Springer, Graz, Austria, 2006, pp. 464–475.
26. Jegou, H., Douze, M., Schmid, C., Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33:1 (2011), 117–128.
27. Demir, B., Bruzzone, L., A novel active learning method in relevance feedback for content-based remote sensing image retrieval. IEEE Trans. Geosci. Remote Sens. 53:5 (2015), 2323–2334.
28. Ferecatu, M., Boujemaa, N., Interactive remote-sensing image retrieval using active relevance feedback. IEEE Trans. Geosci. Remote Sens. 45:4 (2007), 818–826.
29. Jian, M., Jung, C., Shen, Y., Liu, J., Interactive image retrieval using constraints. Neurocomputing 161 (2015), 210–219.
30. J. Wang, A. Kalousis, A. Woznica, Parametric local metric learning for nearest neighbor classification, in: Advances in Neural Information Processing Systems, 2012, pp. 1601–1609.
31. Borwein, J.M., Moors, W.B., Essentially smooth Lipschitz functions. J. Funct. Anal. 149:2 (1997), 305–351.
32. K. Yu, T. Zhang, Y. Gong, Nonlinear learning using local coordinate coding, in: Advances in Neural Information Processing Systems, 2009, pp. 2223–2231.
33. D. Arthur, S. Vassilvitskii, K-means++: the advantages of careful seeding, in: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, Society for Industrial and Applied Mathematics, New Orleans, USA, 2007, pp. 1027–1035.
34. Y. Yang, S. Newsam, Bag-of-visual-words and spatial extensions for land-use classification, in: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM, San Jose, USA, 2010, pp. 270–279.
35. Du, B., Zhang, L., Target detection based on a dynamic subspace. Pattern Recognit. 47:1 (2014), 344–358.
36. B.S. Manjunath, P. Salembier, T. Sikora, Introduction to MPEG-7: Multimedia Content Description Interface, vol. 1, John Wiley & Sons, Chichester, England, 2002.
37. A. Vedaldi, B. Fulkerson, Vlfeat: an open and portable library of computer vision algorithms, in: Proceedings of the International Conference on Multimedia, 2010, pp. 1469–1472.
38. J. Philbin, M. Isard, J. Sivic, A. Zisserman, Descriptor learning for efficient retrieval, in: Computer Vision—ECCV 2010, Springer, Heraklion, Greece, 2010, pp. 677–691.