Have a personal or library account? Click to login
A hierarchical inferential method for indoor scene classification Cover
Open Access
|Jan 2018

References

  1. Alleysson, D., Susstrunk, S. and Herault, J. (2005). Linear demosaicing inspired by the human visual system, IEEE Transactions on Image Processing 14(4): 439-449.10.1109/TIP.2004.84120015825479
  2. Banerji, S., Sinha, A. and Liu, C. (2013). New image descriptors based on color, texture, shape, and wavelets for object and scene image classification, Neurocomputing 117(0): 173-185.10.1016/j.neucom.2013.02.014
  3. Bannour, H. and Hudelot, C. (2012a). Building Semantic Hierarchies Faithful to Image Semantics, Lecture Notes in Computer Science, Vol. 7131, Springer, Berlin/Heidelberg, pp. 4-15.
  4. Bannour, H. and Hudelot, C. (2012b). Hierarchical image annotation using semantic hierarchies, Proceedings of the 21st ACM International Conference on Information and Knowledge Management, Maui, HI, USA, pp. 2431-2434.10.1145/2396761.2398659
  5. Bell, S., Lawrence Zitnick, C., Bala, K. and Girshick, R. (2016). Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 2874-2883.
  6. Bottou, L. (2013). From machine learning to machine reasoning, Machine Learning 94(2): 133-149.10.1007/s10994-013-5335-x
  7. Carneiro, G., Chan, A.B., Moreno, P.J. and Vasconcelos, N. (2007). Supervised learning of semantic classes for image annotation and retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence 29(3): 394-410.10.1109/TPAMI.2007.6117224611
  8. Chaojie,W., Jun, Y. and Dapeng, T. (2013). High-level attributes modeling for indoor scenes classification, Neurocomputing 121: 337-343.10.1016/j.neucom.2013.05.032
  9. Chaves, R., Ramrez, J., Grriz, J. and Illn, I. (2012). Functional brain image classification using association rules defined over discriminant regions, Pattern Recognition Letters 33(12): 1666-1672.10.1016/j.patrec.2012.04.011
  10. Csurka, G., Dance, C., Fan, L., Willamowski, J. and Bray, C. (2004). Visual categorization with bags of keypoints, Workshop on Statistical Learning in Computer Vision, Prague, Czech Republic, Vol. 1, pp. 1-2.
  11. Delaigle, J., Devleeschouwer, C., Macq, B. and Langendijk, L. (2002). Human visual system features enabling watermarking, 2002 IEEE International Conference on Multimedia and Expo. ICME ’02, Los Angeles, CA, USA, Vol. 2, pp. 489-492.
  12. Deng, J., Berg, A.C. and Fei-Fei, L. (2011). Hierarchical semantic indexing for large scale image retrieval, 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Denver, CO, USA, pp. 785-792.
  13. Dixit, M., Chen, S., Gao, D., Rasiwasia, N. and Vasconcelos, N. (2015). Scene classification with semantic fisher vectors, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 2974-2983.
  14. Escobar, M.-J. and Kornprobst, P. (2012). Action recognition via bio-inspired features: The richness of center-surround interaction, Computer Vision and Image Understanding 116(5): 593-605.10.1016/j.cviu.2012.01.002
  15. Farhadi, A., Endres, I., Hoiem, D. and Forsyth, D. (2009). Describing objects by their attributes, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, Miami, FL, USA, pp. 1778-1785.
  16. Faria, D.R., Trindade, P., Lobo, J. and Dias, J. (2014). Knowledge-based reasoning from human grasp demonstrations for robot grasp synthesis, Robotics and Autonomous Systems 62(6): 794-817.10.1016/j.robot.2014.02.003
  17. Fei-Fei, L. and Perona, P. (2005). A Bayesian hierarchical model for learning natural scene categories, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, San Diego, CA, USA, Vol. 2, pp. 524-531.
  18. Felzenszwalb, P.F. and McAllester, D. (2007). The generalized a* architecture, Journal of Artificial Intelligence Research pp. 153-190.
  19. Felzenszwalb, P., Girshick, R. and McAllester, D. (2010a). Cascade object detection with deformable part models, 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA, pp. 2241-2248.10.1109/CVPR.2010.5539906
  20. Felzenszwalb, P., Girshick, R., McAllester, D. and Ramanan, D. (2010b). Object detection with discriminatively trained part-based models, IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9): 1627-1645.10.1109/TPAMI.2009.16720634557
  21. Felzenszwalb, P., McAllester, D. and Ramanan, D. (2008). A discriminatively trained, multiscale, deformable part model, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, Anchorage, AK, USA, pp. 1-8.
  22. Feng, Q., Yuan, C., Pan, J.S., Yang, J.F., Chou, Y.T., Zhou, Y. and Li, W. (2017). Superimposed sparse parameter classifiers for face recognition, IEEE Transactions on Cybernetics 47(2): 378-390.10.1109/TCYB.2016.251623926829813
  23. Feng, Q. and Zhou, Y. (2016). Kernel regularized data uncertainty for action recognition, IEEE Transactions on Circuits and Systems for Video Technology PP(99): 1-1.
  24. Feng, Q., Zhou, Y. and Lan, R. (2016). Pairwise linear regression classification for image set retrieval, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 4865-4872.
  25. Girshick, R.B., Felzenszwalb, P.F. and McAllester, D.A. (2011). Object detection with grammar models, in J. Shawe-Taylor et al. (Eds.), Advances in Neural Information Processing Systems 24, Curran Associates, Inc., Granada, pp. 442-450.
  26. Gupta, P., Arrabolu, S.S., Brown, M. and Savarese, S. (2009). Video scene categorization by 3D hierarchical histogram matching, IEEE 12th International Conference on Computer Vision, Kyoto, Japan, pp. 1655-1662.
  27. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I.H. (2009). The Weka data mining software: An update, ACM SIGKDD Explorations Newsletter 11(1): 10-18.10.1145/1656274.1656278
  28. He, K., Zhang, X., Ren, S. and Sun, J. (2016). Deep residual learning for image recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 770-778.
  29. Hoiem, D., Efros, A.A. and Hebert, M. (2005). Automatic photo pop-up, ACM SIGGRAPH 2005, Los Angeles, CA, USA, pp. 577-584.
  30. Hosang, J., Benenson, R., Doll´ar, P. and Schiele, B. (2016). What makes for effective detection proposals?, IEEE Transactions on Pattern Analysis and Machine Intelligence 38(4): 814-830.10.1109/TPAMI.2015.246590826959679
  31. Huang, K., Tao, D., Yuan, Y., Li, X. and Tan, T. (2011). Biologically inspired features for scene classification in video surveillance, IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics 41(1): 307-313.10.1109/TSMCB.2009.203792320100675
  32. jia Li, L., Su, H., Fei-fei, L. and Xing, E.P. (2010). Object bank: A high-level image representation for scene classification and semantic feature sparsification, in J. Lafferty et al. (Eds.), Advances in Neural Information Processing Systems 23, Curran Associates, Inc., Cambridge, pp. 1378-1386.
  33. Kembhavi, A., Yeh, T. and Davis, L.S. (2010). Why did the person cross the road (there)? Scene understanding using probabilistic logic models and common sense reasoning, in K. Daniilidis et al. (Eds.), Computer Vision-ECCV 2010: 11th European Conference on Computer Vision, Part II, Springer, Berlin/Heidelberg, pp. 693-706.10.1007/978-3-642-15552-9_50
  34. Khan, S., Bennamoun, M., Sohel, F. and Togneri, R. (2014). Geometry Driven Semantic Labeling of Indoor Scenes, Lecture Notes in Computer Science, Vol. 8689, Springer International Publishing, Berlin, pp. 679-694.
  35. Kong, T., Yao, A., Chen, Y. and Sun, F. (2016). Hypernet: Towards accurate region proposal generation and joint object detection, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 845-853.
  36. Lazebnik, S., Schmid, C. and Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, NY, USA, Vol. 2, pp. 2169-2178.
  37. Li-Jia, L., Chong, W., Yongwhan, L., Blei, D.M. and Li, F.-F. (2010). Building and using a semantivisual image hierarchy, 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA, pp. 3336-3343.
  38. Li, L.-J., Su, H., Lim, Y. and Fei-Fei, L. (2014). Object bank: An object-level image representation for high-level visual recognition, International Journal of Computer Vision 107(1): 20-39.10.1007/s11263-013-0660-x
  39. Lin, D., Lu, C., Liao, R. and Jia, J. (2014). Learning important spatial pooling regions for scene classification, 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, pp. 3726-3733.
  40. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y. and Berg, A.C. (2016). SSD: Single Shot Multi-Box Detector, Springer International Publishing, Cham, pp. 21-37.
  41. Liu, Z. and von Wichert, G. (2013). Applying rule-based context knowledge to build abstract semantic maps of indoor environments, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, pp. 5141-5147.
  42. Lorenza Saitta, J.-D.Z. (2013). Abstraction in Artificial Intelligence and Complex Systems, Springer, New York, NY.10.1007/978-1-4614-7052-6
  43. Marszalek, M. and Schmid, C. (2007). Semantic hierarchies for visual object recognition, IEEE Conference on Computer Vision and Pattern Recognition, CVPR’07, Minneapolis, MN, USA, pp. 1-7.
  44. MIT (n.d.) Indoor scene recognition. Dataset, http://web.mit.edu/torralba/www/indoor.html.
  45. Mottaghi, R., Fidler, S., Yao, J., Urtasun, R. and Parikh, D. (2013). Analyzing semantic segmentation using hybrid human-machine CRFS, 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA, pp. 3143-3150.
  46. Neville, J. and Jensen, D. (2007). Relational dependency networks, Journal of Machine Learning Research 8: 653-692.10.7551/mitpress/7432.003.0010
  47. Nguyen, D.T., Ogunbona, P.O. and Li, W. (2013). A novel shape-based non-redundant local binary pattern descriptor for object detection, Pattern Recognition 46(5): 1485-1500.10.1016/j.patcog.2012.10.024
  48. Penatti, O.A., Silva, F.B., Valle, E., Gouet-Brunet, V. and Torres, R.d.S. (2014). Visual word spatial arrangement for image retrieval and classification, Pattern Recognition 47(2): 705-720.10.1016/j.patcog.2013.08.012
  49. Porway, J., Wang, Q. and Zhu, S.C. (2010). A hierarchical and contextual model for aerial image parsing, International Journal of Computer Vision 88(2): 254-283.10.1007/s11263-009-0306-1
  50. Quattoni, A. and Torralba, A. (2009). Recognizing indoor scenes, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, Miami, FL, USA, pp. 413-420.
  51. Ren, X. and Ramanan, D. (2013). Histograms of sparse codes for object detection, 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, pp. 3246-3253.
  52. Ribeiro, M.X., Bugatti, P.H., Traina Jr, C., Marques, P.M.A., Rosa, N.A. and Traina, A.J.M. (2009). Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques, Data and Knowledge Engineering 68(12): 1370-1382.10.1016/j.datak.2009.07.002
  53. Richardson, M. and Domingos, P. (2006). Markov logic networks, Machine Learning 62(1): 107-136.10.1007/s10994-006-5833-1
  54. Rigamonti, R., Brown, M.A. and Lepetit, V. (2011). Are sparse representations really relevant for image classification?, 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA, pp. 1545-1552.
  55. Rigamonti, R., Sironi, A., Lepetit, V. and Fua, P. (2013). Learning separable filters, 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR, USA, pp. 2754-2761.
  56. Sadovnik, A. and Chen, T. (2011). Pictorial structures for object recognition and part labeling in drawings, 18th IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, pp. 3613-3616.
  57. Sharif Razavian, A., Azizpour, H., Sullivan, J. and Carlsson, S. (2014). CNN features off-the-shelf: An astounding baseline for recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, pp. 806-813.
  58. Shotton, J., Blake, A. and Cipolla, R. (2005). Contour-based learning for object detection, 10th IEEE International Conference on Computer Vision, ICCV 2005, Beijing, China, Vol. 1, pp. 503-510.
  59. Siagian, C. and Itti, L. (2007). Rapid biologically-inspired scene classification using features shared with visual attention, IEEE Transactions on Pattern Analysis and Machine Intelligence 29(2): 300-312.10.1109/TPAMI.2007.4017170482
  60. Singla, P. and Domingos, P. (2006). Entity resolution with Markov logic, 6th International Conference on Data Mining, ICDM’06, Hong Kong, China, pp. 572-582.
  61. Tang, J., Zha, Z.-J., Tao, D. and Chua, T.-S. (2012). Semantic-gap-oriented active learning for multilabel image annotation, IEEE Transactions on Image Processing 21(4): 2354-2360.10.1109/TIP.2011.218091622194245
  62. Tang, T. and Qiao, H. (2014). Improving invariance in visual classification with biologically inspired mechanism, Neurocomputing 133(8): 328-341.10.1016/j.neucom.2013.11.003
  63. Teo, C.L., Fermller, C. and Aloimonos, Y. (2015). A Gestaltist approach to contour-based object recognition: Combining bottom-up and top-down cues, International Journal of Robotics Research 34(4-5): 627-652.10.1177/0278364914558493
  64. Vondrick, C., Khosla, A., Malisiewicz, T. and Torralba, A. (2013). HOGgles: Visualizing object detection features, IEEE International Conference on Computer Vision, Sydney, Australia, pp. 1-8.
  65. Welter, P., Riesmeier, J., Fischer, B., Grouls, C., Kuhl, C. and Deserno (n´e Lehmann), T.M. (2011). Bridging the integration gap between imaging and information systems: A uniform data concept for content-based image retrieval in computer-aided diagnosis, Journal of the American Medical Informatics Association 18(4): 506-510.10.1136/amiajnl-2010-000011312839221672913
  66. Xie, L., Tian, Q., Wang, M. and Zhang, B. (2014a). Spatial pooling of heterogeneous features for image classification, IEEE Transactions on Image Processing 23(5): 1994-2008.10.1109/TIP.2014.231011724710400
  67. Xie, L., Wang, J., Guo, B., Zhang, B. and Tian, Q. (2014b). Orientational pyramid matching for recognizing indoor scenes, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, pp. 3734-3741.10.1109/CVPR.2014.477
  68. Xu, M. and Petrou, M. (2010). Learning logic rules for scene interpretation based on Markov logic networks, ACCV 9th Asian Conference on Computer Vision, Xi’an, China, pp. 341-350.
  69. Xu, M., Petrou, M. and Lu, J. (2011). Learning logic rules for the tower of knowledge using Markov logic networks, International Journal of Pattern Recognition and Artificial Intelligence 25(06): 889-907.10.1142/S0218001411008610
  70. Ye, Z., Liu, P., Zhao,W. and Tang, X. (2015). Cognition inspired framework for indoor scene annotation, Journal of Electronic Imaging 24(5): 053013.10.1117/1.JEI.24.5.053013
  71. Yu, J., Rui, Y., Tang, Y.Y. and Tao, D. (2014). High-order distance-based multiview stochastic learning in image classification, IEEE Transactions on Cybernetics 44(12): 2431-2442.10.1109/TCYB.2014.230786225415948
  72. Yu, J., Tao, D., Rui, Y. and Cheng, J. (2013). Pairwise constraints based multiview features fusion for scene classification, Pattern Recognition 46(2): 483-496.10.1016/j.patcog.2012.08.006
  73. Yu, J., Tao, D. and Wang, M. (2012a). Adaptive hypergraph learning and its application in image classification, IEEE Transactions on Image Processing 21(7): 3262-3272.10.1109/TIP.2012.219008322410334
  74. Yu, J., Wang, M. and Tao, D. (2012b). Semisupervised multiview distance metric learning for cartoon synthesis, IEEE Transactions on Image Processing 21(11): 4636-4648.10.1109/TIP.2012.220739522801511
  75. Zhang, C., Liu, J., Tian, Q., Liang, C. and Huang, Q. (2013). Beyond visual features: A weak semantic image representation using exemplar classifiers for classification, Neurocomputing 120(0): 318-324.10.1016/j.neucom.2012.07.056
  76. Zhou, L., Zhou, Z. and Hu, D. (2013). Scene classification using a multi-resolution bag-of-features model, Pattern Recognition 46(1): 424-433.10.1016/j.patcog.2012.07.017
  77. Zhu, Y., Fathi, A. and Fei-Fei, L. (2014). Reasoning about object affordances in a knowledge base representation, in D. Fleet et al. (Eds.), Computer Vision ECCV 2014, Lecture Notes in Computer Science, Vol. 8690, Springer International Publishing, Zurich, pp. 408-42410.1007/978-3-319-10605-2_27
DOI: https://doi.org/10.1515/amcs-2017-0059 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 839 - 852
Submitted on: Oct 31, 2016
|
Accepted on: Jul 16, 2017
|
Published on: Jan 13, 2018
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2018 Jingzhe Jiang, Peng Liu, Zhipeng Ye, Wei Zhao, Xianglong Tang, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.