A hierarchical inferential method for indoor scene classification

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.2004.84120015825479Open DOISearch in Google Scholar

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.2013.02.014Search in Google Scholar

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.Search in Google Scholar

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. 2431-2434.10.1145/2396761.2398659Search in Google Scholar

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.Search in Google Scholar

Bottou, L. (2013). From machine learning to machine reasoning, Machine Learning 94(2): 133-149.10.1007/s10994-013-5335-xSearch in Google Scholar

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.2007.6117224611Open DOISearch in Google Scholar

Chaojie,W., Jun, Y. and Dapeng, T. (2013). High-level attributes modeling for indoor scenes classification, Neurocomputing 121: 337-343.10.1016/j.neucom.2013.05.032Search in Google Scholar

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.2012.04.011Open DOISearch in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.2012.01.002Search in Google Scholar

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.Search in Google Scholar

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.2014.02.003Open DOISearch in Google Scholar

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.Search in Google Scholar

Felzenszwalb, P.F. and McAllester, D. (2007). The generalized a* architecture, Journal of Artificial Intelligence Research pp. 153-190.Search in Google Scholar

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.2010.5539906Search in Google Scholar

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.2009.16720634557Open DOISearch in Google Scholar

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.Search in Google Scholar

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.2016.251623926829813Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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): 10-18.10.1145/1656274.1656278Search in Google Scholar

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.Search in Google Scholar

Hoiem, D., Efros, A.A. and Hebert, M. (2005). Automatic photo pop-up, ACM SIGGRAPH 2005, Los Angeles, CA, USA, pp. 577-584.Search in Google Scholar

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.2015.246590826959679Open DOISearch in Google Scholar

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.2009.203792320100675Open DOISearch in Google Scholar

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.Search in Google Scholar

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_50Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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-xSearch in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

Lorenza Saitta, J.-D.Z. (2013). Abstraction in Artificial Intelligence and Complex Systems, Springer, New York, NY.10.1007/978-1-4614-7052-6Search in Google Scholar

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.Search in Google Scholar

MIT (n.d.) Indoor scene recognition. Dataset, http://web.mit.edu/torralba/www/indoor.html.Search in Google Scholar

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.Search in Google Scholar

Neville, J. and Jensen, D. (2007). Relational dependency networks, Journal of Machine Learning Research 8: 653-692.10.7551/mitpress/7432.003.0010Search in Google Scholar

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.2012.10.024Open DOISearch in Google Scholar

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.2013.08.012Open DOISearch in Google Scholar

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/s11263-009-0306-1Open DOISearch in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.2009.07.002Open DOISearch in Google Scholar

Richardson, M. and Domingos, P. (2006). Markov logic networks, Machine Learning 62(1): 107-136.10.1007/s10994-006-5833-1Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.2007.4017170482Open DOISearch in Google Scholar

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.Search in Google Scholar

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.2011.218091622194245Open DOISearch in Google Scholar

Tang, T. and Qiao, H. (2014). Improving invariance in visual classification with biologically inspired mechanism, Neurocomputing 133(8): 328-341.10.1016/j.neucom.2013.11.003Search in Google Scholar

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): 627-652.10.1177/0278364914558493Search in Google Scholar

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.Search in Google Scholar

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-2010-000011312839221672913Open DOISearch in Google Scholar

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.2014.231011724710400Open DOISearch in Google Scholar

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.2014.477Search in Google Scholar

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.Search in Google Scholar

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/S0218001411008610Open DOISearch in Google Scholar

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.24.5.053013Search in Google Scholar

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.2014.230786225415948Search in Google Scholar

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.2012.08.006Open DOISearch in Google Scholar

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.2012.219008322410334Open DOISearch in Google Scholar

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.2012.220739522801511Search in Google Scholar

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.2012.07.056Search in Google Scholar

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.2012.07.017Open DOISearch in Google Scholar

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_27Search in Google Scholar