Abstract
In this paper, we propose a content-based motion retrieval (CBMR) algorithm, where many-to-many matching method, weighted graph matching, is employed for comparison between two motions. Our novel points can be described as: (1) A selection approach of representative frames (RF) is presented, in this work, each motion is represented by a set of sequence frames, representative frames are first selected from the motions based on Fuzzy clustering and the corresponding initial weights are provided. (2) The RF-based weighted graph model (RF-WGM) is constructed, and a revised KM (Kuhn–Munkres) algorithm is used to solve maximum matching problem of weighted graph. The RF-WGM matching result is used to measure the similarity between two motions. Experimental results and comparison with existing methods show the effectiveness of the proposed algorithm.
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Baak A, Müller M, Seidel H-P (2008) An Efficient algorithm for keyframe-based motion retrieval in the presence of temporal deformations. ACM Conference on Multimedia Information Retrieval pp 451–458
Chakrabarti K, Keogh E, Mehrotra S, Pazzani M (2002) Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans Database Syst 27(2):188–228
Chen C, Yang Y, Nie F, Odobez JM (2011) 3D human pose recovery from image by efficient visual feature selection. Comput Vis Image Underst 115(3):290–299
Chen C, Zhuang Y, Nie F (2011) Learning a 3D human pose distance metric from geometric pose descriptor. IEEE Trans Vis Comput Graph 17(11):1676–1689
Gao Y, Dai Q, Wang M, Zhang N (2011) 3D model retrieval using weighted bipartite graph matching. Signal Process Image Commun 26(1):39–47
Gao Y, Wang M, Zha Z-J, Tian Q, Dai Q, Zhang N (2011) Less is more: efficient 3-D object retrieval with query view selection. IEEE Trans Multimed 13(5):1007–1018
Gao Y, Tang J, Hong R, Yan S, Dai Q, Zhang N, Chua T-S (2012) Camera constraint-free view-based 3-D object retrieval. IEEE Trans Image Process 21(4):2269–2281
Graphics Lab, “Motion Capture Database”, Carnegie Mellon University. http://mocap.cs.cmu.edu/
Gross JL, Yellen J (2011) Graph theory and its applications. 2nd edn. Chapman and Hall/CRC, Boca Raton
Keogh E, Palpanas T, Zordan V, Gunopulos D, Cardle M (2004) ‘Indexing large human-motion databases. Proc VLDB pp 780–791
Kovar L, Gleicher M (2004) Automated extraction and parameterization of motions in large data sets. ACM Trans Graph 23(3):559–568
Kovar L, Gleicher M, Pighin F (2002) Motion Graphs. Proc ACM SIGGRAPH pp 473–482
Krüger B, Tautges J, Weber A, Zinke A (2010) Fast local and global similarity searches in large motion capture databases. Eurographics/ACM SIGGRAPH Symposium on Computer, Animation
Lin Y (2006) Efficient human motion retrieval in large databases. Proc ACM GRAPHITE pp 31–37
Ma Z, Nie F, Yang Y, Uijlings J, Sebe N, Hauptmann AG (2012) Discriminating joint feature analysis for multimedia data understanding. IEEE Trans Multimed 14(6):1662–1672
Müller M, Röder T, Clausen M (2005) Efficient content-based retrieval of motion capture data. ACM Trans Graph 24(3):677–685
Müller M, Röder T (2006) Motion templates for automatic classification and retrieval of motion capture data. Eurographics/ACM SIGGRAPH Symposium on Computer, Animation
Müller M, Röder T (2006) Motion templates for automatic classification and retrieval of motion capture data. Proc ACM SCA
Tam TAM, Lau RWH (2007) Deformable model retrieval based on topological and geometric signatures. IEEE Trans Vis Comput Graph 13(3):470–482
Tian JW, Qi WH, Liu XX (2011) Retrieving deep web data through multi-attributes interfaces with structured queries. Int J Softw Eng Knowl Eng 21(4):523–542
Xiao Q, Wang H, Li F, Gao Y (2011) 3D object retrieval based on a graph model descriptor. Neurocomputing 74(17):2340–2348
Xiao Q, Luo Y, Gao S (2012) Human motion retrieval with symbolic aggregate approximation. In: Proceedings of the Chinese Control and Decision Conference, pp 3632–3636
Yang Y, Zhuang Y, Pan Y (2008) Harmonizing hierarchical manifolds for multimedia document semantics understanding and cross-media retrieval. IEEE Trans Multimed 10(3):437–446
Yang Y, Nie F, Xu D, Luo J, Zhuang Y, Pan Y (2012) A multimedia retrieval framework based on semi-supervised ranking and relevance feedback. IEEE Trans Pattern Anal Mach Intell 34(4):723–742
Zhang WF, Zhou YM, Xu L, Xu BW (2010) A method of detecting web pages based on Hungarian matching algorithm. Chin J Comput 33(10):1963–1975
Zhang Z, Tao D (2012) Slow feature analysis for human action recognition. IEEE Trans Pattern Anal Mach Intell 34(3):436–450
Zhou F, De La Torre F (2012) Factorized graph matching. IEEE Comput Soc Conf Comput Vis Pattern Recognit pp 127–134
Zhou F, De La Torre F (2013) Deformable graph matching. IEEE Comput Soc Conf Comput Vis Pattern Recognit pp 2922–2929.
Acknowledgments
This work was supported by NSFC (No. 60972095, 61271362), Shanxi Province Natural Science Foundation (No. 2012JM8028) and Shanxi Province Education Department Specialized Research Foundation (No. 12JK0510, 12JK0727).
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Communicated by M.J. Watts.
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Xiao, Q., Wang, Y. & Wang, H. Motion retrieval using weighted graph matching. Soft Comput 19, 133–144 (2015). https://doi.org/10.1007/s00500-014-1237-5
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DOI: https://doi.org/10.1007/s00500-014-1237-5