How to involve structural modeling for cartographic object recognition tasks in high-resolution satellite images?

Pattern Recognition Letters

Volumn 31, Issue 10, 2010, Pages 1109-1119

  Erus, Guray ^a   Loménie, Nicolas ^a,b  

^a Université Paris Cité   (France)

^b 21 01 Connexis South Tower   (Singapore)

Author keywords

CBIR; Object recognition; Satellite images; Structural analysis

Indexed keywords

APPEARANCE BASED; CARTOGRAPHIC OBJECTS; COMPACT OBJECTS; DECISION-MAKING SYSTEMS; FALSE ALARM RATE; HIGH LEVEL SEMANTICS; HIGH RESOLUTION SATELLITE IMAGES; IMAGE CHARACTERISTICS; MAN MADE OBJECTS; PERFORMANCE ANALYSIS; SATELLITE IMAGES; SATELLITE SYSTEM; STATISTICAL LEARNING METHODS; STRUCTURAL MODELING; STRUCTURAL SHAPE; VERY HIGH RESOLUTION SATELLITE IMAGES;

DECISION MAKING; MAPPING; OBJECT RECOGNITION; SATELLITES;

STRUCTURAL ANALYSIS;

EID: 77953130571     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2010.01.013     Document Type: Article

References (41)

1. Agarwal, S., Roth, D., 2002. Learning a Sparse Representation for Object Detection. In: ECCV '02: Proc. 7th Eur. Conf. on Computer Vision-Part IV.
2. Bardinet, E., Vidal, S., Arroyo, S., Malandain, G., Blanca, N., 2000. Structural object matching, DECSAI-000303, Spain.
3. Biederman I. Recognition-by-components: A theory of human image understanding. Psychol. Rev. 2 (1987) 115-147
4. Biederman I. Recent psychophysical and neural research in shape recognition. In: Osaka N., Rentschler I., and Biederman I. (Eds). Object Recognition, Attention, and Action (2007), Springer 71-88
5. Canny J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Machine Intell. (1986) 679-698
6. Cao L., Kobayashi Y., and Kuno Y. Spatial relation model for object recognition in human-robot interaction. ICIC 1 (2009) 574-584
7. Cheng Y. Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Machine Intell. 17 8 (1995) 790-799
8. Colliot O., Camara 0., and Bloch I. Integration of fuzzy spatial relations in deformable models - Application to brain MRI segmentation. Pattern Recognition 39 (2006) 1401-1414
9. Comaniciu D., and Meer P. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Machine Intell. 24 5 (2002) 603-619
10. Cordella L.P., Foggia P., Sansone C., and Vento M. Learning structural shape descriptions from examples. Pattern Recognition Lett. 23 12 (2002) 1427-1437
11. Csurka, G., Bray, C., Dance, C., Fan, L., 2004. Visual categorization with bags of keypoints. In: ECCV Internat. Workshop on Statistical Learning in Computer Vision, pp. 1-22.
12. Devernay, F., 1995. A Non-Maxima Suppression Method for Edge Detection with Sub-Pixel Accuracy, INRIA, France, RR-2724, 20 p.
13. Di Ruberto C. Recognition of shapes by attributed skeletal graphs. Pattern Recognition 37 1 (2004) 21-31
14. Dorkó, G., Schmid, C., 2003. Selection of scale-invariant parts for object class recognition. In: ICCV '03: Proc. Ninth IEEE Internat. Conf. on Computer Vision.
15. Douglas D.H., and Peucker T.K. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Canad. Cartograph. 10 (1973) 112-122
16. Erus G., and Lomenie N. Automatic learning of structural models of cartographic objects. IAPR, Graph-Based Representations in Pattern Recognition. Lectures Notes in Computer Science vol. 3434 (2005), Springer 273-280
17. Erus, G., Lomenie, N., 2007. Classification of structural cartographic objects using edge-based features. ISVC07, Lake Tahoe, USA, pp. 385-392.
18. Felzenszwalb P.F., and Huttenlocher D.P. Pictorial structures for object recognition. Int. J. Comput. Vision. 61 1 (2005) 55-79
19. Fergus, R., Perona, P., Zisserman, A., 2003. Object class recognition by unsupervised scale-invariant learning. In: Proc. IEEE Comput. Soc. Conf. on Computer Vision and Pattern Recognition. 264-271.
20. Ferrari V., Fevrier L., Jurie F., and Schmid C. Groups of adjacent contour segments for object detection. IEEE Trans. Pattern Anal. Machine Intell. 30 1 (2008) 36-51
21. Fischler M.A., and Elschlager R.A. The representation and matching of pictorial structures. IEEE Trans. Comput. 22 1 (1973) 67-92
22. Heisele B., Serre T., Pontil M., Vetter T., and Poggio T. Categorization by Learning and Combining Object Parts (2001), MIT Press pp. 1239-1245
23. Hong P., and Huang T.S. Spatial pattern discovery by learning a probabilistic parametric model from multiple attributed relational graphs. Discrete Appl. Math. 139 1-3 (2004) 113-135
24. Hudelot, C., 2005. Towards a Cognitive Vision Platform for Semantic Image Interpretation, Application to the Recognition of Biological Organisms. Universit de Nice Sophia Antipolis - INRIA.
25. Inglada J. Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features. PandRS 62 3 (2007) 236-248
26. Iqbal Q., and Aggarwal J.K. Retrieval by classification of images containing large manmade objects using perceptual grouping. Pattern Recognition J. 35 7 (2002) 1463-1479
27. Leibe, B., Leonardis, A., Schiele, B., 2004. Combined object categorization and segmentation with an implicit shape model. In: ECCV'04 Workshop on Statistical Learning in Computer Vision, May.
28. Li, F., Perona, P., 2005. A Bayesian hierarchical model for learning natural scene categories. In: CVPR '05: Proc. 2005 IEEE Comput. Soc. Conf. on Computer Vision and Pattern Recognition (CVPR'05), vol. 2. pp. 524-531.
29. Lorette A., Descombes X., and Zerubia J. Texture analysis through a Markovian modelling and fuzzy classification: Application to urban area extraction from satellite images. Internat. J. Comput. Vision 36 3 (2000) 221-236
30. Mayer, H., Hinz, S., Bacher, U., Baltsavias, E., 2006. A test of automatic road extraction approaches, PCV06.
31. Opelt, A., Pinz, A., Zisserman, A., 2006. A boundary-fragment-model for object detection. In: Proc. Eur. Conf. on Computer Vision.
32. Petrakis, E.G.M., Faloutsos, C., 1995. Similarity Searching in Large Image Databases, Department of Computer Science, University of Maryland.
33. Sangineto E. An abstract representation of geometric knowledge for object classification. Pattern Recognition Lett. 24 9-10 (2003) 1241-1250
34. Shao Z., and Kittler J. Shape representation and recognition based on invariant unary and binary relations. Image Vision Comput. 17 5-6 (1999) 429-444
35. Shotton J., Blake A., and Cipolia R. Multi-scale categorical object recognition using contour fragments. IEEE Trans. Pattern Anal. Machine Intell. (2007)
36. Tax, D.M.J., 2001. One-class classification: Concept-learning in the absence of counter-examples. PhD Thesis. Delft University of Technology, Delft, The Netherlands.
37. Trias-Sanz R., and Loménie N. Automatic bridge detection in high-resolution satellite images. 3rd Internat. Conf. on Computer Vision Systems (ICVS'03). LNCS (2003), Springer 172-181
38. Tupin F., Maitre F., Mangin J.-F., Nicolas J.M., and Pechersky E. Detection of linear features in SAR images: application to the road network extraction. IEEE Trans. Geosci. Remote Sensing 36 2 (1998) 434-453
39. Viola P., and Jones M.J. Robust real-time face detection. Internat. J. Comput. Vision 57 2 (2004) 137-154
40. Wang, Q., Seabra-Lopes, L., Tax, D.M.J., 2004. Visual object recognition through one-class learning. In: Proc. ICIAR'04, vol. I, pp. 463-470.
41. Weber M., Welling M., and Perona P. Unsupervised learning of models for recognition. ECCV '00: Proc. 6th Eur. Conf. on Computer Vision-Part I (2000), Springer-Verlag 18-32