Abstract
In the problem of object category recognition, we have studied different families of descriptors exploiting RGB and 3D information. Furthermore, 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 image texture-based. 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 [2] and achieve, with a single descriptor type, an accuracy close to state-of-art learning based approaches using combined descriptors.
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Proença, P.F., Gaspar, F., Dias, M.S. (2013). Good Appearance and Shape Descriptors for Object Category Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41914-0_38
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DOI: https://doi.org/10.1007/978-3-642-41914-0_38
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