Semantic Classification of Heterogeneous Urban Scenes Using Intrascene Feature Similarity and Interscene Semantic Dependency

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 8, Issue 5, 2015, Pages 2005-2014

  Zhang, Xiuyuan ^a   Du, Shihong ^a   Wang, Yi Chen ^b  

^a PEKING UNIVERSITY   (China)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

High dimensional features fusion; multinomial distribution; scene classification

Indexed keywords

NEAREST NEIGHBOR SEARCH;

AFFINE TRANSFORMATIONS; CLASSIFICATION APPROACH; CLASSIFICATION RESULTS; HIGH DIMENSIONAL FEATURE; MULTINOMIAL DISTRIBUTIONS; SCENE CLASSIFICATION; SEMANTIC CLASSIFICATION; SIMILARITY MEASUREMENTS;

SEMANTICS;

IMAGE CLASSIFICATION; NEAREST NEIGHBOR ANALYSIS; PATTERN RECOGNITION; REMOTE SENSING; SEGMENTATION; URBAN AREA;

EID: 85027944830     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2015.2414178     Document Type: Article

References (34)

1. M. R. Boutell, J. Luo, X. Shen, and C. M. Brown, "Learning multi-label scene classification, " Pattern Recognit., vol. 37, no. 9, pp. 1757-1771, 2004.
2. K. Werner, "Cognitive and linguistic ideas in geographic information semantics, " in Cognitive and Linguistic Aspects of Geographic Space. Berlin, Germany: Springer-Verlag, 2013, pp. 159-174.
3. L. Zhang, X. T. Zhen, and L. Shao, "Learning object-to-class kernels for scene classification, " IEEE Trans. Image Process., vol. 23, no. 8, pp. 3241-3253, Aug. 2014.
4. A. Pablo et al. "Semantic segmentation using regions and parts, " in Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR'02), 2012, pp. 3378-3385.
5. P. Deli, Z. Li, R. Ji, and F. Sun, "Efficient semantic image segmentation with multi-class ranking prior, " Comput. Vis. Image Understand., vol. 120, pp. 81-90, 2014.
6. L. J. Li, H. Su, L. Fei-Fei, and E. P. Xing, "Object bank: A high-level image representation for scene classification & semantic feature sparsification, " in Proc. Adv. Neural Inf. Process. Syst., 2010, pp. 1378-1386.
7. A. Torralba, R. Fergus, and W. T. Freeman, "80 Million tiny images: A large data set for nonparametric object and scene recognition, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 11, pp. 1958-1970, Nov. 2008.
8. N. Serrano, A. E. Savakis, and J. Luo, "Improved scene classification using efficient low-level features and semantic cues, " Pattern Recognit., vol. 37, no. 9, pp. 1773-1784, 2004.
9. A. A. Popescu, I. Gavat, and M. Datcu, "Contextual descriptors for scene classes in very high resolution SAR images, " IEEE Geosci. Remote Sens. Lett., vol. 9, no. 1, pp. 80-84, Jan. 2012.
10. A. C. Berg and J. Malik, "Geometric blur for template matching, " in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog. (CVPR'01), 2001, vol. 1, pp. 607-614.
11. L. F. Posada, K. K. Narayanan, F. Hoffmann, and T. Bertram, "Semantic classification of scenes and places with omnidirectional vision, " in Proc. IEEE Eur. Conf. Mobile Robots (ECMR'13), 2013, pp. 113-118.
12. C. Couprie, C. Farabet, L. Najman, and Y. LeCun, "Indoor semantic segmentation using depth information, " in Proc. 1st Int. Conf. Learn. Representations (ICLR'13), 2013, pp. 1-8.
13. T. S. Pan, A. T. Sokolich, P. Lin, S. Abdel-Ghaffar, and S. J. Bishop, "Rapid semantic categorization of scenes: An advantage for emotional images?" J. Vis., vol. 13, no. 9, pp. 1312-1312, 2013.
14. N. Ikizler-Cinbis and S. Sclaroff, "Object, scene and actions: Combining multiple features for human action recognition, " in Proc. Eur. Conf. Comput. Vis. (ECCV'10), 2010, pp. 494-507.
15. L. Fei-Fei and P. Perona, "A bayesian hierarchical model for learning natural scene categories, " in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog. (CVPR'05), 2005, vol. 2, pp. 524-531.
16. W. A. Cheung and G. Hamarneh, "Scale invariant feature transform for n-dimensional images (n-SIFT), " IEEE Trans. Image Process., vol. 18, no. 9, pp. 2012-2021, Sep. 2007.
17. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, " in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog., 2006, vol. 2, pp. 2169-2178.
18. K. Q. Weinberger, J. Blitzer, and L. K. Saul, "Distance metric learning for large margin nearest neighbor classification, " in Proc. Adv. Neural Inf. Process. Syst., 2005, pp. 1473-1480.
19. S. A. Dudani, "The distance-weighted k-nearest-neighbor rule, " IEEE Trans. Syst. Man Cybern., vol. 6, no. 4, pp. 325-327, Apr. 1976.
20. P. Tsyurmasto, M. Zabarankin, and S. Uryasev, "Value-at-risk support vector machine: Stability to outliers, " J. Comb. Optim., vol. 28, no. 1, pp. 218-232, 2014.
21. M. Pal and P. M. Mather, "Support vector machines for classification in remote sensing, " Int. J. Remote Sens., vol. 26, no. 5, pp. 1007-1011, 2005.
22. M. Pal, "Random forest classifier for remote sensing classification, " Int. J. Remote Sens., vol. 26, no. 1, pp. 217-222, 2005.
23. D. M. Blei, "Probabilistic models of text and images, " Ph.D. dissertation, Univ. California, Berkeley, CA, USA, 2004.
24. S. J. Gershman and D. M. Blei, "A tutorial on Bayesian nonparametric models, " J. Math. Psychol., vol. 56, no. 1, pp. 1-12, 2012.
25. D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation, " J. Mach. Learn. Res., vol. 3, pp. 993-1022, 2003.
26. M. Lienou, H. Maitre, and M. Datcu, "Semantic annotation of satellite images using latent dirichlet allocation, " IEEE Geosci. Remote Sens. Lett., vol. 7, no. 1, pp. 28-32, Jan. 2010.
27. D. Larlus, J. Verbeek, and F. Jurie, "Category level object segmentation by combining bag-of-words models with dirichlet processes and random fields, " Int. J. Comput. Vis., vol. 88, no. 2, pp. 238-253, 2010.
28. K. A. de Souza Gazolli and E. O. Teatini Salles, "Exploring neighborhood and spatial information for improving scene classification, " Pattern Recognit. Lett., vol. 46, pp. 83-88, 2014.
29. M. Gerke and N. Kerle, "Automatic structural seismic damage assessment with airborne oblique Pictometry© imagery, " Photogramm. Eng. Remote Sens., vol. 77, no. 9, pp. 885-898, 2011.
30. R. Guha and J. H. Van Drie, "Structure-activity landscape index: Identifying and quantifying activity cliffs, " J. Chem. Inf. Model., vol. 8, no. 3, pp. 646-658, 2008.
31. X. Luo, G. Wu, and K. Kita, "Learning distance metrics with feature space performance for image retrieval, " in Proc. 2013 Int. Conf. Elect. Inf. Technol. Rail Transp. (EITRT'13), 2014, vol. 2, pp. 411-421.
32. X. Zhang, J. Li, and H. Yu, "Local density adaptive similarity measurement for spectral clustering, " Pattern Recognit. Lett., vol. 32, no. 2, pp. 352-358, 2011.
33. A. M. Qamar and E. Gaussier, "RELIEF algorithm and similarity learning for k-NN, " Int. J. Comput. Inf. Syst. Ind. Manage. Appl. (IJCISIM), vol. 4, pp. 445-458, 2012.
34. P. de Valpine and A. N. Harmon-Threatt, "General models for resource use or other compositional count data using the Dirichlet-multinomial distribution, " Ecology, vol. 94, no. 12, pp. 2678-2687, 2013.