• DocumentCode
    2736150
  • Title

    Ensembling Neural Networks-based 3d model retrieval

  • Author

    Liu, Yujie ; Li, Zongmin ; Li, Hua

  • Author_Institution
    Sch. Of Comput. Sci. & Commun. Eng., China Univ. Of Pet., Dongying
  • Volume
    1
  • fYear
    2008
  • fDate
    6-8 Oct. 2008
  • Firstpage
    456
  • Lastpage
    460
  • Abstract
    How to retrieve more and more 3D models is the important point in pervasive computing. In this paper, a novel ensembling neural network (NN) - based 3D model retrieval method is proposed. Firstly, four NNs are trained by constructed learning algorithm (CLA). These four NNs are trained by using difference feature of models. And then NN assembles are employed to retrieve 3D models. The experiments show that the supervised learning increased the power of retrieval.
  • Keywords
    image retrieval; learning (artificial intelligence); neural nets; solid modelling; ubiquitous computing; 3D model retrieval; constructed learning algorithm; ensembling neural network; pervasive computing; supervised learning; Assembly; Content based retrieval; Feature extraction; Feedback; Linear discriminant analysis; Neural networks; Pervasive computing; Shape; Supervised learning; Support vector machines; 3D model retrieval; Ensembling; Neural Network; Pervasive Computing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pervasive Computing and Applications, 2008. ICPCA 2008. Third International Conference on
  • Conference_Location
    Alexandria
  • Print_ISBN
    978-1-4244-2020-9
  • Electronic_ISBN
    978-1-4244-2021-6
  • Type

    conf

  • DOI
    10.1109/ICPCA.2008.4783631
  • Filename
    4783631
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2736150