Contributo in atti di convegno, 2014, ENG, 10.2312/3dor.20141057
Biasotti S.; Cerri A.; Abdelrahman M.; Aono M.; Ben Hamza A.; El-Melegy M.; Farag A.; Garro V.; Giachetti D.; Giorgi D.; Godil A.; Li C.; Liu Y.; Martono H.; Sanada C.; Tatsuma A.; Velasco-Forero S.; Xu C.
CNR-IMATI, Genova, Italy; CNR-IMATI, Genova, Italy; Computer Vision and Image Processing Laboratory (CVIP Lab), University of Louisville, KY, USA; Department of Computer Science and Engineering, Toyohashi University of Technology, Japan; Concordia University, Canada; Computer Vision and Image Processing Laboratory (CVIP Lab), University of Louisville, KY, USA; Computer Vision and Image Processing Laboratory (CVIP Lab), University of Louisville, KY, USA; Dipartimento di Informatica, Università di Verona, Italy; Dipartimento di Informatica, Università di Verona, Italy; CNR-ISTI, Pisa; National Institute of Standards and Technology, Gaithersburg, MD, USA; National Institute of Standards and Technology, Gaithersburg, MD, USA; Department of Computer Science and Technology, Tsinghua University, the People's Republic of China; Department of Computer Science and Engineering, Toyohashi University of Technology, Japan; Department of Computer Science and Engineering, Toyohashi University of Technology, Japan; Department of Computer Science and Engineering, Toyohashi University of Technology, Japan; Department of Mathematics, National University of Singapore, Singapore; Department of Computer Science and Technology, Tsinghua University, the People's Republic of China;
This paper reports the results of the SHREC'14 track: Retrieval and classification on textured 3D models, whose goal is to evaluate the performance of retrieval algorithms when models vary either by geometric shape or texture, or both. The collection to search in is made of 572 textured mesh models, having a two-level classification based on geometry and texture. Together with the dataset, a training set of 96 models was provided. The track saw eight participants and the submission of 22 runs, to either the retrieval or the classification contest, or both. The evaluation results show a promising scenario about textured 3D retrieval methods, and reveal interesting insights in dealing with texture information in the CIELab rather than in the RGB colour space.
Eurographics Workshop on 3D Object Retrieval (2014), pp. 111–120, Strasbourg, France, April 6, 2014
Information Storage and Retrieval: Content Analysis and Indexing, Abstracting methods
Cerri Andrea, Biasotti Silvia Maria, Giorgi Daniela
IMATI – Istituto di matematica applicata e tecnologie informatiche "Enrico Magenes", ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 280241
Year: 2014
Type: Contributo in atti di convegno
Creation: 2014-05-06 12:12:40.000
Last update: 2019-01-30 14:38:00.000
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:280241
DOI: 10.2312/3dor.20141057
Scopus: 2-s2.0-85018232364