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• Astikainen, K., Holm, L., Pitkänen, E., et al.: Towards structured output prediction of enzyme function. In: Proc. of BMC’08, BioMed Central, vol. 2, p. S2 (2008)
• Baldassi, C., Alemi-Neissi, A., Pagan, M., et al.: Shape similarity, better than semantic membership, accounts for the structure of visual object representations in a population of monkey inferotemporal neurons. PLOS Comput. Biol. 9(e1003), 167 (2013)
• Barutcuoglu, Z., DeCoro, C.: Hierarchical shape classification using Bayesian aggregation. In: SMI’06, IEEE, pp. 44–44 (2006)
• Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. PAMI 24(24), 509–522 (2002)
• Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernel-based vector machines. JMLR 2, 265–292 (2001)
• Dimitrovski, I., Kocev, D., Loskovska, S., Džeroski, S.: Hierarchical annotation of medical images. Pattern Recognit. 44(10), 2436–2449 (2011)
• Eisner, R., Poulin, B., Szafron, D., et al. (2005) Improving protein function prediction using the hierarchical structure of the gene ontology. In: Proc. of CIBCB’05, IEEE, pp 1–10
• Ekman, P., Rosenberg, E.L.: What the face reveals: basic and applied studies of spontaneous expression using the facial action coding system (FACS). Oxford University Press, Oxford (1997)
• Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. PAMI 28(4), 594–611 (2006)
• Griffin, G., Perona, P.: Learning and using taxonomies for fast visual categorization. In: Proc. of CVPR’08, IEEE, pp. 1–8 (2008)
• Hoyoux, T., Rodríguez-Sánchez, A.J., Piater, J.H., Szedmak, S.: Can computer vision problems benefit from structured hierarchical classification? In: Proc. of CAIP’15, pp. 403–414. Springer, Berlin (2015)
• Kiritchenko, S., Matwin, S., Famili, A.F.: Functional annotation of genes using hierarchical text categorization. In: Proc. of BioLINK SIG: LLIKB’05 (2005)
• Lampert, C.H., Nickisch, H., Harmeling, S.: Attribute-based classification for zero-shot visual object categorization. PAMI 36(3), 453–465 (2014)
• Lee, J.H., Downie, J.S.: Survey of music information needs, uses, and seeking behaviours: preliminary findings. In: Proc. of ISMIR’04, Citeseer, vol 2004, p. 5 (2004)
• Lee, T.S., Mumford, D.: Hierarchical bayesian inference in the visual cortex. JOSA A 20(7), 1434–1448 (2003)
• Li, L.J., Wang, C., Lim, Y., et al.: Building and using a semantivisual image hierarchy. In: Proc. of CVPR’10, IEEE, pp. 3336–3343 (2010)
• Lucey, P., Cohn, J.F., Kanade, T., et al: The extended Cohn–Kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression. In: Proc. of CVPRW’10, pp. 94–101 (2010)
• Marszałek, M., Schmid, C.: Constructing category hierarchies for visual recognition. In: Proc. of ECCV’08, pp. 479–491. Springer, Berlin (2008)
• Mittelman, R., Sun, M., Kuipers, B., Savarese, S.: A Bayesian generative model for learning semantic hierarchies. Frontiers in psychology 5, 417 (2014)
• Rodríguez-Sánchez, A., Tsotsos, J.: The roles of endstopped and curvature tuned computations in a hierarchical representation of 2D shape. PLOS One 7(8), 1–13 (2012)
• Rodríguez-Sánchez, A.J., Tsotsos, J.K.: The importance of intermediate representations for the modeling of 2d shape detection: Endstopping and curvature tuned computations. In: Proc. of CVPR’11, IEEE, pp. 4321–4326 (2011)
• Rodríguez-Sánchez, A.J., Szedmak, S., Piater, J.: SCurV: A 3D descriptor for object classification. In: Proc. of IROS’15 (2015)
• Ruiz, M.E., Srinivasan, P.: Hierarchical text categorization using neural networks. Inf. Retr. 5(1), 87–118 (2002)
• Rusu, R., Bradski, G., Thibaux, R., Hsu, J.: Fast 3D recognition and pose using the viewpoint feature histogram. In: Proc. of IROS’10, pp. 2155–2162 (2010)
• Rusu, R.B., Cousins, S.: 3D is here: Point cloud library (PCL). In: Proc. on ICRA’11, pp. 1–4 (2011)
• Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M.: Robust object recognition with cortex-like mechanisms. PAMI 29(3), 411–426 (2007)
• Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The Princeton Shape Benchmark. In: Proc. of SMI’04, pp. 167–178 (2004)
• Silla Jr., C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. DMKD 22(1–2), 31–72 (2011)
• Snow, R., Jurafsky, D., Ng, A.Y.: Semantic taxonomy induction from heterogeneous evidence. In: Proc. of ACL’06, association for computational linguistics, pp. 801–808 (2006)
• Tombari, F., Salti, S., Di Stefano, L.: Unique shape context for 3D data description. In: Proc. of ACM’10, ACM, pp. 57–62 (2010)
• Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: Proc. of ICML’04, ACM, p. 104 (2004)
• Wang, X., Liu, Y., Zha, H.: Intrinsic spin images: a subspace decomposition approach to understanding 3D deformable shapes. In: Proc. of 3DPVT’10, vol. 10, pp. 17–20 (2010)
• Weidenbacher, U., Neumann, H.: Extraction of surface-related features in a recurrent model of V1–V2 interactions. PLOS One 4(6), e5909 (2009)
• Wohlkinger, W., Vincze, M.: Ensemble of shape functions for 3D object classification. In: Proc. of ROBIO’11, pp. 2987–2992 (2011)
• Yamane, Y., Carlson, E., Bowman, K., et al.: A neural code for three-dimensional object shape in macaque inferotemporal cortex. Nat. Neurosci. 11(11), 1352–1360 (2008)