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Proença, P., Gaspar, F. & Dias, J. (2015). Good Appearance and 3D Shape Descriptors for Object Category Recognition. International Journal on Artificial Intelligence Tools. 24 (4), 1540017
P. F. Proenca et al., "Good Appearance and 3D Shape Descriptors for Object Category Recognition", in Int. Journal on Artificial Intelligence Tools, vol. 24, no. 4, pp. 1540017, 2015
@article{proenca2015_1716171740240, author = "Proença, P. and Gaspar, F. and Dias, J.", title = "Good Appearance and 3D Shape Descriptors for Object Category Recognition", journal = "International Journal on Artificial Intelligence Tools", year = "2015", volume = "24", number = "4", doi = "10.1142/s0218213015400175", pages = "1540017", url = "http://www.scopus.com/inward/record.url?eid=2-s2.0-84940062420&partnerID=MN8TOARS" }
TY - JOUR TI - Good Appearance and 3D Shape Descriptors for Object Category Recognition T2 - International Journal on Artificial Intelligence Tools VL - 24 IS - 4 AU - Proença, P. AU - Gaspar, F. AU - Dias, J. PY - 2015 SP - 1540017 SN - 0218-2130 DO - 10.1142/s0218213015400175 UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-84940062420&partnerID=MN8TOARS AB - For the problem of object category recognition, we have studied different families of descriptors exploiting RGB and 3D information. We have proven practically that 3D shape-based descriptors are more suitable for this type of recognition due to low shape intra-class variance, as opposed to texture-based. Performance evaluation on training-set subsampling, suggests that the viewpoint invariance characteristics of 3D descriptors, favors significantly these descriptors while invariant SIFT descriptors can be ambiguous. In addition, we have also shown how an efficient Naive Bayes Nearest Neighbor (NBNN) classifier can scale to a large hierarchical RGB-D Object Dataset and achieve, with a single descriptor type, an accuracy close to state-of-the-art learning-based approaches using combined descriptors. ER -