Motion Retrieval Based on Multiple Instance Learning by Isomap and RBF

In this paper, a new learning method is proposed for human motion data analysis. In order to train motion data by the method of multiple instance learning, each human joint's motion clip is regarded as a bag, while each of its segments is regarded as an instance. Due to high dimensionality of motion's features, Isomap nonlinear dimensionality reduction is used. An algorithmic framework is used to approximate the optimal mapping function by a radial basis function (RBF) neural network for handling new data. Then data driven decision trees based on multiple instance are automatically constructed to reflect the influence of each point during the comparison of motion similarity. Some experimental examples are given to demonstrate the effectiveness and efficiency of the proposed algorithm.

[1]  R. Cleve,et al.  Quantum fingerprinting. , 2001, Physical review letters.

[2]  Adam D. Smith,et al.  Authentication of quantum messages , 2001, The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings..

[3]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[4]  Hwayean Lee,et al.  Arbitrated quantum signature scheme with message recovery , 2004 .

[5]  Yueting Zhuang,et al.  Data-driven Generation of Decision Tree based on Ensemble Multiple-instance Learning for Motion Retrieval , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[6]  L. Miles,et al.  2000 , 2000, RDH.

[7]  Wei-Ying Ma,et al.  Learning an image manifold for retrieval , 2004, MULTIMEDIA '04.

[8]  Guihua Zeng,et al.  Quantum key agreement protocol , 2004 .

[9]  Shor,et al.  Simple proof of security of the BB84 quantum key distribution protocol , 2000, Physical review letters.

[10]  Feng Liu,et al.  3D motion retrieval with motion index tree , 2003, Comput. Vis. Image Underst..

[11]  Guihua Zeng,et al.  Arbitrated quantum-signature scheme , 2001, quant-ph/0109007.

[12]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[13]  Chih-Yi Chiu,et al.  Content-based retrieval for human motion data , 2004, J. Vis. Commun. Image Represent..

[14]  Dominic Mayers,et al.  Unconditional security in quantum cryptography , 1998, JACM.

[15]  Ashwin Srinivasan,et al.  Multi-instance tree learning , 2005, ICML.