Sparse Modeling of Human Actions from Motion Imagery
Tóm tắt
Từ khóa
Tài liệu tham khảo
Aharon, M., Elad, M., & Bruckstein, A. (2006). K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11), 4311–4322.
Blake, R., & Shiffrar, M. (2007). Perception of human motion. Annual Review of Psychology, 58(1), 47–73.
Bruckstein, A., Donoho, D., & Elad, M. (2009). From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Review, 51(1), 34–81.
Cadieu, C., & Olshausen, B. A. (2008). Learning transformational invariants from natural movies. In NIPS (pp. 209–216).
Castrodad, A., Xing, Z., Greer, J., Bosch, E., Carin, L., & Sapiro, G. (2011). Learning discriminative sparse representations for modeling, source separation, and mapping of hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 49(11), 4263–4281.
Charles, A., Olshausen, B., & Rozell, C. (2011). Learning sparse codes for hyperspectral imagery. IEEE Journal of Selected Topics in Signal Processing.
Chen, C., Ryoo, M. S., & Aggarwal, J. K. (2010). UT-Tower dataset: aerial view activity classification challenge. http://cvrc.ece.utexas.edu/SDHA2010/Aerial_View_Activity.html .
Dalal, N., & Triggs, B. (2006). Human detection using oriented histograms of flow and appearance. In ECCV.
Dean, T., Washington, R., & Corrado, G. (2009). Recursive sparse, spatiotemporal coding. In ISM (pp. 645–650).
Dollar, P., Rabaud, V., Cottrell, G., & Belongie, S. (2005). 2nd joint IEEE international workshop on behavior recognition via sparse spatio-temporal features. In Visual surveillance and performance evaluation of tracking and surveillance (pp. 65–72).
Donoho, D. L. (2000). High-dimensional data analysis: the curses and blessings of dimensionality. In American Mathematical Society conference math challenges of the 21st century.
Gall, J., Yao, A., Razavi, N., van Gool, L., & Lempitsky, V. (2011). Hough forests for object detection, tracking, and action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(11), 2188–2202.
Gregor, K., & LeCun, Y. (2010). Learning fast approximations of sparse coding. In ICML (pp. 399–406).
Guo, K., Ishwar, P., & Konrad, J. (2010). Action recognition using sparse representation on covariance manifolds of optical flow. In AVSS (pp. 188–195).
Ikizler-Cinbis, N., & Sclaroff, S. (2010). Object, scene and actions: combining multiple features for human action recognition. In ECCV (pp. 494–507).
Jhuang, H., Serre, T., Wolf, L., & Poggio, T. (2007). A biologically inspired system for action recognition. In ICCV (pp. 1–8).
Kläser, A., Marszałek, M., & Schmid, C. (2008). A spatio-temporal descriptor based on 3d-gradients. In BMVC.
Kovashka, A., & Grauman, K. (2010). Learning a hierarchy of discriminative space-time neighborhood features for human action recognition. In CVPR (pp. 2046–2053).
Laptev, I., & Lindeberg, T. (2003). Space-time interest points. In ICCV (pp. 432–439).
Laptev, I., Marszałek, M., Schmid, C., & Rozenfeld, B. (2008). Learning realistic human actions from movies. In CVPR.
Le, Q., Zou, W., Yeung, S., & Ng, A. (2011). Learning hierarchical spatio-temporal features for action recognition with independent subspace analysis. In CVPR.
Liu, J., Luo, J., & Shah, M. (2009). Recognizing realistic actions from videos “in the wild”. In CVPR.
Mairal, J., Bach, F., Ponce, J., Sapiro, G., & Zisserman, A. (2008). Supervised dictionary learning. In NIPS (pp. 1033–1040).
Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2010). Online learning for matrix factorization and sparse coding. Journal of Machine Learning Research, 11, 19–60.
Marszałek, M., Laptev, I., & Schmid, C. (2009). Actions in context. In CVPR.
Ramirez, I., Sprechmann, P., & Sapiro, G. (2010). Classification and clustering via dictionary learning with structured incoherence and shared features. In CVPR (pp. 3501–3508).
Rodriguez, M. D., Ahmed, J., & Shah, M. (2008). Action Mach: a spatio-temporal maximum average correlation height filter for action recognition. In CVPR.
Ryoo, M., Chen, C., Aggarwal, J., & Chowdhury, R. A. (2010). An overview of contest on semantic description of human activities (sdha) 2010. In ICPR-contests (pp. 270–285).
Schuldt, C., Laptev, I., & Caputo, B. (2004). Recognizing human actions: a local svm approach. In ICPR (pp. 32–36).
Scovanner, P., Ali, S., & Shah, M. (2007). A 3-dimensional sift descriptor and its application to action recognition. In ACM multimedia (pp. 357–360).
Shao, L., & Mattivi, R. (2010). Feature detector and descriptor evaluation in human action recognition. In CIVR (pp. 477–484).
Sprechmann, P., & Sapiro, G. (2010). Dictionary learning and sparse coding for unsupervised clustering. In ICASSP.
Taylor, G., Fergus, R., Le Cun, Y., & Bregler, C. (2010). Convolutional learning of spatio-temporal features. In ECCV (pp. 140–153).
Tibshirani, R. (1994). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B, 58, 267–288.
Vezzani, R., Davide, B., & Cucchiara, R. (2010). HMM based action recognition with projection histogram features. In ICPR (pp. 286–293).
Wang, H., Ullah, M. M., Kläser, A., Laptev, I., & Schmid, C. (2009). Evaluation of local spatio-temporal features for action recognition. In BMVC.
Wang, H., Kläser, A., Schmid, C., & Cheng-Lin, L. (2011). Action recognition by dense trajectories. In CVPR (pp. 3169–3176).
Willems, G., Tuytelaars, T., & van Gool, L. (2008). An efficient dense and scale-invariant spatio-temporal interest point detector. In ECCV (pp. 650–663).
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2008). Robust face recognition via sparse representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(2), 210–227.
Xiang, Z., Xu, H., & Ramadge, P. (2011). Learning sparse representations of high dimensional data on large scale dictionaries. In NIPS (pp. 900–908).
Yeffet, L., & Wolf, L. (2009). Local trinary patterns for human action recognition. In ICCV (pp. 492–497).