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Nonlinear dimensionality reduction with hybrid distance for trajectory representation of dynamic texture. (English) Zbl 1194.94112

Summary: Dynamic textures play an important role in video content analysis. Current works of dynamic textures mainly focus on overall texture and motion analysis for segmentation or classification based on statistical features and structure models. This paper proposes a novel framework to study the dynamic textures by exploring the motion trajectory using unsupervised learning. A nonlinear dimensionality reduction algorithm, called hybrid distance isometric embedding (HDIE), is proposed, to generate a low-dimensional motion trajectory from high-dimensional feature space of the raw video data. First, we partition the high-dimensional data points into a set of data clusters and construct the intra-cluster graphs based on the individual character of each data cluster to build the basic layer of HDIE. Second, we construct the inter-cluster graph by analyzing the interrelation among these isolated data clusters to build the top layer of HDIE. Finally, we generate a whole graph and map all data points into a unique low-dimensional feature space, trying to maintain the distances of all pairs of high-dimensional data points. Experiments on the standard dynamic texture database show that the proposed framework with the novel algorithm can represent the motion characters of the dynamic textures very well.

MSC:

94A12 Signal theory (characterization, reconstruction, filtering, etc.)

Software:

clusfind; darch
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References:

[1] Tuceryan, M.; Jain, A. K.: Texture analysis. The handbook of pattern recognition and computer vision, (1998)
[2] L.Y. Wei, Deterministic texture analysis and synthesis using tree structure vector quantization, in: Proceedings of the XII Brazilian Symposium on Computer Graphics and Image Processing, 1999, pp. 207–213.
[3] M. Szummer, R.W. Picard, Temporal texture modeling, in: Proceedings of the IEEE International Conference on Image Processing, September 1996, pp. 823–826.
[4] Doretto, G.; Chiuso, A.; Wu, Y. N.; Soatto, S.: Dynamic textures, International journal of computer vision 51, No. 2, 91-109 (2003) · Zbl 1030.68646 · doi:10.1023/A:1021669406132
[5] D. Chetverikov, R. Péteri, A brief survey of dynamic texture description and recognition, in: Proceedings of the 4th International Conference on Computer Recognition Systems, Poland, 2005, pp. 17–26.
[6] G. Doretto, D. Cremers, P. Favaro, S. Soatto, Dynamic texture segmentation, in: IEEE International Conference on Computer Vision, vol. 2, 2003, pp. 1236–1242.
[7] L. Yuan, F. Weng, C. Liu, H.-Y. Shum, Synthesizing dynamic texture with closed-loop linear dynamic system, in: Proceedings of the European Conference on Computer Vision, vol. 2, Prague, Czech Republic, 2004, pp. 603–616.
[8] P. Saisan, G. Doretto, Y. Wu, S. Soatto, Dynamic texture recognition, in: IEEE International Conference on Computer Vision and Pattern Recognition, vol. 2, 2001, pp. 58–63.
[9] Vishwanathan, S. V. N.; Smola, A. J.; Vidal, R.: Binet-Cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes, International journal of computer vision 73, No. 1, 95-119 (2007)
[10] Fablet, R.; Bouthemy, P.: Motion recognition using nonparametric image motion models estimated from temporal and multiscale co-occurrence statistics, IEEE transactions on pattern analysis and machine intelligence 25, 1619-1624 (2003)
[11] R. Peteri, D. Chetverikov, Dynamic texture recognition using normal flow and texture regularity, in: Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis, 2005, pp. 223–230.
[12] Peh, C. H.; Cheong, L. -F.: Synergizing spatial and temporal texture, IEEE transactions on image processing 11, 1179-1191 (2002)
[13] Polana, R.; Nelson, R.: Temporal texture and activity recognition, Motion-based recognition, 87-115 (1997)
[14] A. Calway, Estimating the structure of textured surfaces using local affine flow, in: Proceedings of the British Machine Vision Conference, 2000, pp. 92–101.
[15] K. Otsuka, T. Horikoshi, S. Suzuki, M. Fujii, Feature extraction of temporal texture based on spatiotemporal motion trajectory, in: IEEE International Conference on Pattern Recognition, vol. 2, 1998, pp. 1047–1051.
[16] J.R. Smith, C.-Y. Lin, M. Naphade, Video texture indexing using spatiotemporal wavelets, in: IEEE International Conference on Image Processing, vol. 2, 2002, pp. 437–440.
[17] R.P. Wildes, J.R. Bergen, Qualitative spatiotemporal analysis using an oriented energy representation, in: Proceedings of the European Conference on Computer Vision, 2000, pp. 768–784.
[18] \langlehttp://www.cwi.nl/projects/dyntex/index.html angle.
[19] M. Koskela, M. Sjoberg, J. Laaksonen, V. Viitaniemi, H. Muurinen, Rushes summarization with self-organizing maps, in: Proceedings of ACM International Workshop on TRECVID Video Summarization, 2007, pp. 45–49.
[20] M.A. Carreira-Perpinan, A review of dimension reduction techniques, Technical Report CS-96-09, Department of Computer Science, University of Sheffield, UK, 1996.
[21] L.J.P. van der Maaten, E.O. Postma, H.J. van den Herik, Dimensionality reduction: a comparative review, submitted to neurocomputing.
[22] Hotelling, H.: Analysis of a complex of statistical variables into principal components, Journal of educational psychology 24, 417-441 (1933) · JFM 59.1182.04
[23] Scholkopf, B.; Smola, A. J.; Muller, K. R.: Nonlinear component analysis as a kernel eigenvalue problem, Neural computation 10, No. 5, 1299-1319 (1998)
[24] Kohonen, T.: Self-organizing maps, (2001) · Zbl 0957.68097
[25] Hinton, G. E.; Salakhutdinov, R. R.: Reducing the dimensionality of data with neural networks, Science 313, 504-507 (2006) · Zbl 1226.68083 · doi:10.1126/science.1127647
[26] M.A. Carreira-Perpinan, Z. Lu, Dimensionality reduction by unsupervised regression, IEEE International Conference on Computer Vision and Pattern Recognition, 2008, pp. 1–8.
[27] Seung, H. S.; Lee, D. D.: The manifold ways of perception, Science 290, 2268-2269 (December 2000)
[28] Tenenbaum, J. B.; Silva, V.; Langford, J. C.: A global geometric framework for nonlinear dimensionality reduction, Science 290, 2319-2323 (December 2000)
[29] Roweis, S. T.; Saul, L. K.: Nonlinear dimensionality reduction by locally linear embedding, Science 290, 2323-2326 (December 2000)
[30] Belkin, M.; Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation, Neural computation 15, No. 6, 1373-1396 (2003) · Zbl 1085.68119 · doi:10.1162/089976603321780317
[31] J.A. Lee, A. Lendasse, N. Donckers, M. Verleysen, A robust nonlinear projection method, in: Proceedings of the Eighth European Symposium on Artificial Neural Networks, 2000, pp. 13–20.
[32] Lee, J. A.; Lendasse, A.; Verleysen, M.: Nonlinear projection with curvilinear distances: isomap versus curvilinear distance analysis, Neurocomputing 57, 49-76 (2004)
[33] G. Hinton, S. Roweis, Stochastic neighbor embedding, in: Advances in Neural Information Processing Systems, vol. 15, MIT Press, Cambridge, MA, 2002, pp. 833–840.
[34] K.Q. Weinberger, F. Sha, L.K. Saul, Learning a kernel matrix for nonlinear dimensionality reduction, in: Proceedings of the 21st International Conference on Machine Learning, 2004, pp. 839–846.
[35] Y.W. Teh, S.T. Roweis, Automatic alignment of hidden representations, in: Advances in Neural Information Processing Systems, vol. 15, MIT Press, Cambridge, MA, 2002, pp. 841–848.
[36] M. Brand, Charting a manifold, in: Advances in Neural Information Processing Systems, vol. 15, MIT Press, Cambridge, MA, 2002, pp. 985–992.
[37] Zhang, Z.; Zha, H.: Principal manifolds and nonlinear dimensionality reduction via local tangent space alignment, SIAM journal on scientific computing 26, No. 1, 313-338 (2004) · Zbl 1077.65042 · doi:10.1137/S1064827502419154
[38] W. Fan, D.-Y. Yeung, Locally linear models on face appearance manifolds with application to dual-subspace based classification, in: IEEE International Conference on Computer Vision and Pattern Recognition, 2006, pp. 1384–1390.
[39] A. Hadid, M. Pietikäinen, From still image to video-based face recognition: an experimental analysis, in: The Sixth International Conference on Automatic Face and Gesture Recognition, 2004, pp. 813–818.
[40] K.C. Lee, J. Ho, M.H. Yang, D. Kriegman, Video-based face recognition using probabilistic appearance manifolds, in: IEEE International Conference on Computer Vision and Pattern Recognition, June 2003, pp. 313–320.
[41] R. Wang, S. Shan, X. Chen, W. Gao, Manifold–manifold distance with application to face recognition based on image set, in: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, USA, June 2008, pp. 1–8.
[42] R. Pless, Image spaces and video trajectories: using isomap to explore video sequences, in: Proceedings of IEEE International Conference on Computer Vision, 2003, pp. 1433–1440.
[43] T. Stich, M. Magnor, Keyframe animation from video, in: Proceedings of the IEEE International Conference on Image Processing, 2006, pp. 2713–2716.
[44] Y. Liu, Y. Liu, K.C.C. Chan, Multiple video trajectories representation using double-layer isometric feature mapping, in: Proceedings of the IEEE International Conference on Multimedia and Expo, 2008, pp. 129–132.
[45] Fisher, R. A.: The use of multiple measurements in taxonomic problems, Annals of eugenics 7, 179-188 (1936)
[46] Jain, A. K.: Algorithms for clustering data, (1988) · Zbl 0665.62061
[47] Kaufman, L.; Rousseeuw, P. J.: Finding groups in data: an introduction to cluster analysis, (1990) · Zbl 1345.62009
[48] J.B. MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, Berkeley, 1967, pp. 281–297. · Zbl 0214.46201
[49] Zahn, C.: Graph-theoretical methods for detecting and describing gestalt clusters, IEEE transactions on computers 20, 68-86 (1971) · Zbl 0264.68040 · doi:10.1109/T-C.1971.223083
[50] T. Asano, B. Bhattacharya, M. Keil, F. Yao, Clustering algorithms based on minimum and maximum spanning trees, in: Proceedings of the Fourth Annual Symposium on Computational Geometry, 1988, pp. 252–257.
[51] O. Grygorash, Y. Zhou, Z. Jorgensen, Minimum spanning tree based clustering algorithms, in: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence, 2006, pp. 73–81.
[52] M. Ester, H.P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD), 1996, pp. 226–231.
[53] Johnson, S. C.: Hierarchical clustering schemes, Psychometrika 2, 241-254 (1967) · Zbl 1367.62191
[54] Jain, A. K.; Murty, M. N.; Flynn, P. J.: Data clustering: a review, ACM computing surveys 31, No. 3, 264-323 (1999)
[55] M.A. Peltier, B. Dubuisson, A fuzzy clustering algorithm based on the k-nearest neighbors rule for the detection of evolution, in: Proceedings of International Conference on Systems, Man and Cybernetics, vol. 4, 1993, pp. 696–701.
[56] Battiato, S.; Blasi, G. D.; Reforgiato, R. D.: Advanced indexing schema for imaging applications: three-case studies, IET image processing 1, No. 3, 249-268 (September 2007)
[57] J. Zhong, S. Scarlaroff, Temporal texture recognition model using 3D features, Technical Report, MIT Media Lab Perceptual Computing, 2002.
[58] Yang, L.: Building k edge-disjoint spanning trees of minimum total length for isometric data embedding, IEEE transactions on pattern analysis and machine intelligence 27, No. 10, 1680-1683 (October 2005)
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