Dynamic Scene Recognition with Complementary Spatiotemporal Features
暂无分享,去创建一个
Richard P. Wildes | Axel Pinz | Christoph Feichtenhofer | A. Pinz | Christoph Feichtenhofer | R. Wildes
[1] Jean Ponce,et al. Learning mid-level features for recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[2] Andrew Zisserman,et al. Three things everyone should know to improve object retrieval , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[3] A. W. M. van den Enden,et al. Discrete Time Signal Processing , 1989 .
[4] Trevor Darrell,et al. Beyond spatial pyramids: Receptive field learning for pooled image features , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[5] C. V. Jawahar,et al. Blocks That Shout: Distinctive Parts for Scene Classification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[6] Richard P. Wildes,et al. Spacetime Stereo and 3D Flow via Binocular Spatiotemporal Orientation Analysis , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[7] Andrei Zaharescu,et al. Anomalous Behaviour Detection Using Spatiotemporal Oriented Energies, Subset Inclusion Histogram Comparison and Event-Driven Processing , 2010, ECCV.
[8] Florent Perronnin,et al. Fisher Kernels on Visual Vocabularies for Image Categorization , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[9] Martin Szummer,et al. Temporal texture modeling , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.
[10] Mubarak Shah,et al. Scene Modeling Using Co-Clustering , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[11] Yihong Gong,et al. Locality-constrained Linear Coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[12] Edward H. Adelson,et al. The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..
[13] Matthieu Cord,et al. Dynamic Scene Classification: Learning Motion Descriptors with Slow Features Analysis , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[14] Thomas S. Huang,et al. Image Classification Using Super-Vector Coding of Local Image Descriptors , 2010, ECCV.
[15] Hans Knutsson,et al. Signal processing for computer vision , 1994 .
[16] Nuno Vasconcelos,et al. Probabilistic kernels for the classification of auto-regressive visual processes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[17] P. Lions,et al. Axioms and fundamental equations of image processing , 1993 .
[18] Anil K. Jain,et al. Image classification for content-based indexing , 2001, IEEE Trans. Image Process..
[19] Antonio Torralba,et al. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.
[20] Richard P. Wildes,et al. The Applicability of Spatiotemporal Oriented Energy Features to Region Tracking , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[21] P. Perona,et al. Rapid natural scene categorization in the near absence of attention , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[22] Rama Chellappa,et al. Moving vistas: Exploiting motion for describing scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[23] Bernt Schiele,et al. International Journal of Computer Vision manuscript No. (will be inserted by the editor) Semantic Modeling of Natural Scenes for Content-Based Image Retrieval , 2022 .
[24] Nuno Vasconcelos,et al. Scene classification with low-dimensional semantic spaces and weak supervision , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[25] Richard P. Wildes,et al. Spacetime Forests with Complementary Features for Dynamic Scene Recognition , 2013, BMVC.
[26] Cordelia Schmid,et al. Actions in context , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[27] Nicolas Le Roux,et al. Ask the locals: Multi-way local pooling for image recognition , 2011, 2011 International Conference on Computer Vision.
[28] Richard P. Wildes,et al. Dynamically encoded actions based on spacetime saliency , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Thomas V. Papathomas,et al. Motion perception with spatiotemporally matched chromatic and achromatic information reveals a “slow” and a “fast” motion system , 1993, Vision Research.
[30] E H Adelson,et al. Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.
[31] Cordelia Schmid,et al. Aggregating Local Image Descriptors into Compact Codes , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[32] Richard P. Wildes,et al. Dynamic scene understanding: The role of orientation features in space and time in scene classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[33] Martin Szummer,et al. Indoor-outdoor image classification , 1998, Proceedings 1998 IEEE International Workshop on Content-Based Access of Image and Video Database.
[34] Andrew Zisserman,et al. The devil is in the details: an evaluation of recent feature encoding methods , 2011, BMVC.
[35] Cordelia Schmid,et al. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[36] S. Thorpe,et al. How parallel is visual processing in the ventral pathway? , 2004, Trends in Cognitive Sciences.
[37] Thomas Mensink,et al. Improving the Fisher Kernel for Large-Scale Image Classification , 2010, ECCV.
[38] Richard P. Wildes,et al. Bags of Spacetime Energies for Dynamic Scene Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[39] James V. Stone. Vision and Brain: How We Perceive the World , 2012 .
[40] F. Xavier Roca,et al. Compact and adaptive spatial pyramids for scene recognition , 2012, Image Vis. Comput..
[41] Richard P. Wildes,et al. Action Spotting and Recognition Based on a Spatiotemporal Orientation Analysis , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[42] S. Engel,et al. Colour tuning in human visual cortex measured with functional magnetic resonance imaging , 1997, Nature.
[43] Lorenzo Torresani,et al. C3D: Generic Features for Video Analysis , 2014, ArXiv.
[44] Stefano Soatto,et al. Dynamic Textures , 2003, International Journal of Computer Vision.
[45] Thomas Serre,et al. Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[46] M. Potter,et al. Recognition memory for a rapid sequence of pictures. , 1969, Journal of experimental psychology.
[47] Richard P. Wildes,et al. Qualitative Spatiotemporal Analysis Using an Oriented Energy Representation , 2000, ECCV.
[48] Richard P. Wildes,et al. Spacetime Texture Representation and Recognition Based on a Spatiotemporal Orientation Analysis , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[49] Yi Yang,et al. Weakly supervised sparse coding with geometric consistency pooling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[50] Pietro Perona,et al. A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[51] Bingbing Ni,et al. Geometric ℓp-norm feature pooling for image classification , 2011, CVPR 2011.
[52] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[53] Andrei Gorea,et al. Two carriers for motion perception: Color and luminance , 1991, Vision Research.
[54] Andrew B. Watson,et al. A look at motion in the frequency domain , 1983 .