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| Publications | |
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2018 |
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Self-Supervised Generation of Spatial Audio for 360° Video |
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Towards Realistic Predictors |
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RESOUND: Towards Action Recognition without Representation Bias |
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Feature Space Transfer for Data Augmentation |
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Cascade R-CNN: Delving into High Quality Object Detection |
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2017 |
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Deep Scene Image Classification with the MFAFVNet |
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AGA: Attribute Guided Augmentation |
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Deep Learning with Low Precision by Half-wave Gaussian Quantization |
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Semantically Consistent Regularization for Zero-Shot Recognition |
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2016 |
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Parametric Regression on the Grassmannian |
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Complex Activity Recognition via Attribute Dynamics |
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Object based Scene Representations using Fisher Scores of Local Subspace Projections |
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Large Margin Discriminant Dimensionality Reduction in Prediction Space |
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Boosted Convolutional Neural Networks |
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Semantic Clustering for Robust Fine-Grained Scene Recognition |
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A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection |
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Peak-Piloted Deep Network for Facial Expression Recognition |
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VLAD^3: Encoding Dynamics of Deep Features for Action Recognition |
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Person-Following UAVs |
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Pedestrian Detection Aided by Temporal Prior |
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2015 |
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A View of Margin Losses as Regularizers of Probability
Estimates |
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Bayesian Model Adaptation for Crowd Counts |
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Generic Promotion of Diffusion-Based Salient Object Detection |
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Learning Complexity-Aware Cascades for Deep Pedestrian Detection |
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Scene Classification with Semantic Fisher Vectors |
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Multiple Instance Learning for Soft Bags via Top Instances |
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How many bits does it take for a stimulus to be salient? |
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A Real-time Cascade Pedestrian Detection based on Heterogeneous Features |
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2014 |
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| Robust Deformable and Occluded Object Tracking with Dynamic Graph Zhaowei Cai, Longyin Wen, Zhen Lei, Nuno Vasconcelos, and Stan Z. Li IEEE Transactions on Image Processing Vol 23(Dec):5497-5509, 2014. [pdf] [project] |
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Object Recognition with Hierarchical Discriminant Saliency Networks |
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| Boosting Algorithm for Learning Detector Cascade Mohammad J. Saberian and Nuno Vasconcelos Journal of Machine Learning Research (JMLR) Vol 15(Jul):2569-2605, 2014. [pdf] |
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Cross-modal Domain Adaptation for Text-based Regularization of Image Semantics in Image Retrieval Systems |
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On the Role of Correlation and Abstraction in Cross-Modal Multimedia Retrieval |
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Anomaly Detection and Localization in Crowded Scenes |
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| Multi-Resolution Cascades for Multiclass Object Detection Mohammad Saberian and Nuno Vasconcelos in Advances in Neural Information Processing Systems (NIPS) 2014 [pdf] |
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Geodesic Regression on the Grassmannian
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| Guess-Averse Loss Functions For Cost-Sensitive Multiclass Boosting Mohammad Saberian, Oscar Beijbom, David Kriegman and Nuno Vasconcelos in International Conference on Machine Learning (ICML), 2014 [pdf] [supp] [code] |
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Learning Receptive Fields for Pooling from Tensors of Feature Response |
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Learning optimal seeds for diffusion-based salient object detection |
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Using Context to Improve Cascaded Pedestrian Detection |
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2013 |
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Latent Dirichelet Allocation Models for Image Classification |
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Localizing Target Structures in Ultrasound Video - a Phantom Study |
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Biologically-inspired Object Tracking Using Center-surround Mechanisms |
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| Surveillance of Crowded Environments: Modeling the Crowd by its Global Properties A. Chan and N. Vasconcelos in Modeling, Simulation and Visual Analysis of Crowds |
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Class-Specific Simplex-Latent Dirichlet Allocation for Image Classification |
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Dynamic Pooling for Complex Event Recognition |
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Recognizing Activities via Bag of Words for Attribute Dynamics |
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| 2012 | |
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Minimum Probability of Error Image Retrieval: From Visual Features to Image Semantics |
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Endoscopic image analysis in semantic space |
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| Learning Optimal Embedded Cascades Mohammad J. Saberian and Nuno Vasconcelos IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 34(10), 2005-2018, October 2012 [ps] [pdf] |
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| Holistic Context Models for Visual Recognition N. Rasiwasia and N. Vasconcelos IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 34 (5), 902-917, May 2012 © IEEE [ps] [pdf] [supplement/ps] [supplement/pdf] |
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Counting People with Low-level Features and Bayesian Regression |
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On the connections between saliency and tracking |
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Scene Recognition on the Semantic Manifold |
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| Recognition in Ultrasound Videos: Where am I? R. Kwitt, N. Vasconcelos, S. Razzaque and S. Aylward Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'12), (Young Scientist award) Nice, 2012. [ps] [pdf] |
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| On the Regularization of Image Semantics by Modal Expansion J. Costa Pereira, and Nuno Vasconcelos Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, 2012. © IEEE [ps] [pdf] [demo] |
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| Boosting Algorithms for Simultaneous Feature Extraction and Selection Mohammad J. Saberian and Nuno Vasconcelos Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, 2012. © IEEE [ps] [pdf][Code] |
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| 2011 | |
| Generalized Stauffer-Grimson Background Subtraction for Dynamic Scenes A. B. Chan, V. Mahadevan and N. Vasconcelos Machine Vision and Applications, vol. 22(5), 751-766, 2011. [ps] [pdf] |
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| Multiclass Boosting: Theory and Algorithms Mohammad J. Saberian and Nuno Vasconcelos . In Proc. Neural Information Processing Systems (NIPS), Granada, Spain, Dec 2011. [ps] [pdf][code] |
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| Maximum Covariance Unfolding - Manifold Learning for Bimodal Data V. Mahadevan, C-W. Wong, J Costa-Pereira, T.T. Liu, N. Vasconcelos and L.K. Saul In Proc. Neural Information Processing Systems Dec 2011 [ps] [pdf] |
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| Learning Pit Pattern Concepts for Gastroenterological Training R. Kwitt, N. Rasiwasia, N. Vasconcelos, A. Uhl, M. Hafner, F. Wr Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'11), Toronto, Sept 2011 [ps] [pdf] |
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| Biologically Plausible Detection of Amorphous Objects in the Wild Sunhyoung Han and Nuno Vasconcelos, IEEE CVPR Workshop on Biologically-Consistent Vision, pp. 17-24, June 2011.© IEEE [ps][pdf] |
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| Automatic Initialization and Tracking Using Attentional Mechanisms V. Mahadevan and N. Vasconcelos, IEEE CVPR Workshop on Biologically-Consistent Vision, June 2011.© IEEE [ps][pdf] |
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| Adapted Gaussian Models for Image Classification M. Dixit, N. Rasiwasia and N. Vasconcelos Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, 2011. © IEEE [ps] [pdf] |
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| TaylorBoost: First and Second Order Boosting Algorithms with Explicit Margin Control Mohammad J. Saberian, Hamed Masnadi-Shirazi and Nuno Vasconcelos Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, 2011. © IEEE [ps] [pdf][code] |
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| 2010 | |
| Cost-Sensitive Boosting Hamed Masnadi-Shirazi and Nuno Vasconcelos IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 32(2), 294, March 2010 . © IEEE [ps] [pdf] |
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| Biologically Plausible Saliency Mechanisms Improve Feedforward Object Recognition Sunhyoung Han and Nuno Vasconcelos Vision Research, vol. 50(22), 2295-2307, October 2010 [pdf] [doi:10.1016/j.visres.2010.05.034] |
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| A Novel Approach to FRUC using Discriminant Saliency and Frame Segmentation N. Jacobson, Y-L. Lee, V. Mahadevan, N. Vasconcelos and T.Q. Nguyen IEEE Transactions on Image Processing, vol. 19(11) ,2924-2934, Nov. 2010. © IEEE [ps] [pdf] |
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| Spatiotemporal Saliency in Highly Dynamic Scenes V. Mahadevan and N. Vasconcelos IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 32(1), 171-177, January 2010. © IEEE [ps] [pdf] [dataset] |
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| Variable margin losses for classifier design. Hamed Masnadi-Shirazi and Nuno Vasconcelos . In Proc. Neural Information Processing Systems (NIPS), Vancouver, Canada, Dec 2010. [ps] [pdf] |
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| A biologically plausible network for the Computation of Orientation Dominance Kritika Muralidharan and Nuno Vasconcelos . In Proc. Neural Information Processing Systems (NIPS), Vancouver, Canada, Dec 2010. [ps] [pdf] |
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| Boosting Classifer Cascades Mohammad J. Saberian and Nuno Vasconcelos . In Proc. Neural Information Processing Systems (NIPS), Vancouver, Canada, Dec 2010. [ps] [pdf] |
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| A New Approach to Cross-Modal Multimedia Retrieval N. Rasiwasia, J. Costa Pereira, E. Coviello, G. Doyle, G.R.G. Lanckriet, R. Levy, N. Vasconcelos ACM Proceedings of the 15th international conference on Multimedia, (best student paper award) Florence, Italy, Oct 2010. [ps] [pdf] |
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| Motion Vector Refinement for FRUC Using Saliency and Segmentation N. Jacobson, Y-L. Lee, V. Mahadevan, N. Vasconcelos and T.Q. Nguyen IEEE International Conference on Multimedia & Expo (ICME), Singapore, Jul 2010 © IEEE [ps] [pdf] |
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| Risk minimization, probability elicitation, and cost-sensitive SVMs Hamed Masnadi-Shirazi and Nuno Vasconcelos Proc. International Conference on Machine Learning (ICML), June 2010. [ps] [pdf] |
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| On the Design of Robust Classifiers for Computer Vision Hamed Masnadi-Shirazi, Vijay Mahadevan and Nuno Vasconcelos Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, 2010. © IEEE [ps] [pdf] |
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| Anomaly Detection in Crowded Scenes Vijay Mahadevan, Wei-Xin LI, Viral Bhalodia and Nuno Vasconcelos Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, 2010. © IEEE [ps] [pdf] [dataset] |
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| 2009 | |
| Layered Dynamic Textures Antoni B. Chan and Nuno Vasconcelos IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 31(10),1862-1879, October 2009. © IEEE [ps] [pdf] |
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| Bayesian Poisson Regression for Crowd Counting Antoni B. Chan and Nuno Vasconcelos IEEE International Conference on Computer Vision, Kyoto, September 2009. © IEEE [ps] [pdf] |
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| Discriminant saliency, the detection of suspicious coincidences, and applications to visual recognition D. Gao, S. Han, N. Vasconcelos, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31(6), pp. 989-1005, June 2009. © IEEE [ps][pdf] [doi:10.1109/TPAMI.2009.27] |
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| Fluoroscopic tumour tracking for image-guided lung cancer radiotherapy T. Lin, L. Cervino, X. Tang, N. Vasconcelos, and S. Jiang. Physics in Medicine and Biology, Vol 54(4), 981-992, February 2009. [ps][pdf] [doi:10.1088/0031-9155/54/4/011] |
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| Minimum Bayes error features for visual recognition G. Carneiro and N. Vasconcelos Image and Vision Computing, Vol 27(1), 131-140, January 2009. [ps][pdf] |
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| Decision-theoretic saliency: computational principles, biological plausibility, and implications for neurophysiology and psychophysics D. Gao and N. Vasconcelos. Neural Computation, 21, 239-271, January 2009. [ps][pdf] |
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| Natural Image Statistics and Low-complexity Feature Selection M. Vasconcelos and N. Vasconcelos IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 31(2), pp. 228-244, February 2009. © IEEE [ps][pdf] |
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| Holistic Context Modeling using Semantic Co-occurrences N. Rasiwasia and N. Vasconcelos In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, June 2009. © IEEE [pdf] |
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| Variational Layered Dynamic Textures A. B. Chan and N. Vasconcelos In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, June 2009. © IEEE [pdf] |
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| Saliency Based Discriminant Tracking V. Mahadevan and N. Vasconcelos In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, June 2009. © IEEE [pdf] |
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| Analysis of Crowded Scenes using Holistic Properties A. B. Chan, M. Morrow, and N. Vasconcelos In 11th IEEE Intl. Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2009), Miami, June 2009. © IEEE [pdf] |
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| Derivations for the Layered Dynamic Texture and Temporally-Switching Layered Dynamic Texture A. B. Chan and N. Vasconcelos Technical Report SVCL-TR-2009-01, June 2009. [pdf] |
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| 2008 | |
| On the plausibility of the discriminant center-surround hypothesis for visual saliency D. Gao, V. Mahadevan, and N. Vasconcelos. Journal of Vision, 8(7):13, 1-18, June 2008. [doi:10.1167/8.7.13.] [ps][pdf] |
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| Modeling, clustering, and segmenting video with mixtures of dynamic textures A. B. Chan and N. Vasconcelos. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 30(5), pp. 909-926, May 2008. © IEEE [ps][pdf] |
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| On the Design of Loss Functions for Classification: theory, robustness to outliers, and SavageBoost. Hamed Masnadi-Shirazi and Nuno Vasconcelos . In Proc. Neural Information Processing Systems (NIPS), Vancouver, Canada, Dec 2008. [ps] [pdf] |
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| Unsupervised Moving Target Detection in Dynamic Scenes V. Mahadevan and N. Vasconcelos. Army Science Conference, Orlando, FL, Dec 2008. [ps][pdf] |
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| Image Retrieval using Query by Contextual Example N. Rasiwasia and N. Vasconcelos. ACM International Conference on Multimedia Information Retrieval (ACM-MIR), Vancouver, Canada, Oct 2008. [ps][pdf] |
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| Complex discriminant features for object classification S. Han and N. Vasconcelos. International Conference on Image Processing (ICIP), (best student paper award) San Diego, California, Oct. 2008. © IEEE [ps][pdf] |
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| A Systematic Study of the role of Context on Image Classification N. Rasiwasia and N. Vasconcelos. International Conference on Image Processing (ICIP), San Diego, California, Oct. 2008. © IEEE [ps][pdf] |
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| Privacy Preserving Crowd Monitoring: Counting People without People Models or Tracking A. B. Chan, Z. S. J. Liang, and N. Vasconcelos. In, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, June 2008. © IEEE, [ps][pdf] |
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| Scene Classification with Low-dimensional Semantic Spaces and Weak Supervision N. Rasiwasia and N. Vasconcelos. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, June 2008. © IEEE, [ps][pdf] |
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| Background Subtraction in Highly Dynamic Scenes V. Mahadevan and N. Vasconcelos. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, June 2008. © IEEE, [ps][pdf] |
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| A Study of Query by Semantic Example N. Rasiwasia and N. Vasconcelos. 3rd International Workshop on Semantic Learning and Applications in Multimedia, Anchorage, June 2008. © IEEE, [ps][pdf] |
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| Object-based regions of interest for image compression S. Han and N. Vasconcelos, Data Compression Conference (DCC), pp 132-141 Snowbird, Utah, Mar. 2008. [ps] [pdf] |
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| 2007 | |
| Bridging the Gap: Query by Semantic Example Rasiwasia, N., Moreno, P. L., Vasconcelos, N. Multimedia, IEEE Transactions on, Vol. 9(5), pp. 923-938, Aug 2007.© IEEE,[ps][pdf] |
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| From Pixels to Semantic Spaces: Advances in Content-Based Image Retrieval N. Vasconcelos IEEE Computer, Vol. 40(7), pp. 20-26, July 2007.© IEEE,[ps][pdf] |
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| Supervised Learning of Semantic Classes for Image Annotation and Retrieval G. Carneiro, A. B. Chan, P. J. Moreno, and N. Vasconcelos IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29(3), pp. 394-410, March 2007.© IEEE,[ps][pdf] |
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| The discriminant center-surround hypothesis for bottom-up saliency. D. Gao, V. Mahadevan and N. Vasconcelos. In Proc. Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2007. [ps] [pdf] |
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| High Detection-rate Cascades for Real-Time Object Detection. Hamed Masnadi-Shirazi and Nuno Vasconcelos. Proceedings of IEEE International Conference on Computer Vision (ICCV) , Rio de Janeiro, Brazil, October 2007. © IEEE, [ps] [pdf] |
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| Bottom-up saliency is a discriminant process D. Gao and N. Vasconcelos. Proceedings of IEEE International Conference on Computer Vision (ICCV) , Rio de Janeiro, Brazil, October 2007. © IEEE, [ps] [pdf] |
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| Discriminant Interest Points are Stable D. Gao and N. Vasconcelos. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, MN, June 2007. © IEEE, [ps][pdf] |
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| Classifying Video with Kernel Dynamic Textures A. B. Chan and N. Vasconcelos. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, MN, June 2007. © IEEE, [ps][pdf] |
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| Asymmetric Boosting Hamed Masnadi-Shirazi and Nuno Vasconcelos. Proceedings of International Conference on Machine Learning, Corvallis, OR, June 2007. [ps][pdf] |
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| Direct Convex Relaxations of Sparse SVM A. B. Chan, N. Vasconcelos, and G. R. G. Lanckriet. Proceedings of International Conference on Machine Learning, Corvallis, OR, June 2007. [ps][pdf] (updated version) (this old version accidently truncated the last dimension of the "wine" dataset [ps][pdf]) |
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| Supplemental for "Classifying Video with Kernel Dynamic Textures" A. B. Chan and N. Vasconcelos. Technical Report SVCL-TR-2007-03, April, 2007. [ps][pdf][zip w/ video] |
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| Duals of the QCQP and SDP Sparse SVM A. B. Chan, N. Vasconcelos, and G. R. G. Lanckriet. Technical Report SVCL-TR-2007-02, April 2007. [ps][pdf] |
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| 2006 | |
| Image Compression using Object-based Regions of Interest S. Han, N. Vasconcelos Proceedings of the International Conference on Image Processing pp. 3097-3100 Atlanta, Georgia, October 2006. © IEEE [ps][pdf] |
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| Using Statistics to Search and Annotate Pictures: an Evaluation of Semantic Image Annotation and Retrieval on Large Databases A. B. Chan, P. J. Moreno, and N. Vasconcelos Proceedings of Joint Statistical Meetings (JSM), Seattle, August 2006. [ps][pdf] |
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| Query By Semantic Example Nikhil Rasiwasia, Nuno Vasconcelos, Pedro J Moreno Proceedings of the International Conference on Image and Video Retrieval LNCS 4071, pp. 51-60 Phoenix, Arizona, July 2006. [ps][pdf] |
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| Weakly Supervised Top-Down Image Segmentation M. Vasconcelos, G. Carneiro, and N. Vasconcelos Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, New York, June 2006.© IEEE,[ps][pdf] |
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Single Image Superresolution Based on Support Vector Regression |
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| 2005 | |
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A Multiresolution Manifold Distance for Invariant Image Similarity |
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| Layered Dynamic Textures A. B. Chan and N. Vasconcelos, Proceedings of Neural Information Processing Systems 19, Vancouver, December 2005. [ps][pdf] |
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Mixtures of Dynamic Textures |
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| A Database Centric View of Semantic Image Annotation and Retrieval G. Carneiro and N. Vasconcelos, Proceedings of ACM Conference on Research and Development in Information Retrieval (ACM SIGIR) Salvador, Brazil, August 2005. [ps][pdf] |
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Probabilistic Kernels for the Classification of Auto-Regressive Visual Processes |
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Integrated learning of saliency, complex features, and object detectors from cluttered scenes |
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| Formulating Semantic Image Annotation as a Supervised Learning Problem G. Carneiro and N. Vasconcelos Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, June 2005.© IEEE ,[ps][pdf] |
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Classification and Retrieval of Traffic Video using Auto-Regressive Stochastic Processes |
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Minimum Bayes Error Features for Visual Recognition by Sequential Feature Selection and Extraction |
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The EM Algorithm for Layered Dynamic Textures |
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A Bayesian Architecture for Combining Saliency Detectors |
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| 2004 | |
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Minimum Probability of Error Image Retrieval |
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| On the Efficient Evaluation of Probabilistic Similarity Functions for Image Retrieval N. Vasconcelos, IEEE Transactions on Information Theory vol. 50, No.7, pp1482-1496, July 2004. © IEEE,[ps][pdf] |
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Discriminant Saliency for Visual Recognition from Cluttered Scenes |
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| Scalable Discriminant Feature Selection for Image Retrieval and Recognition N. Vasconcelos and M. Vasconcelos Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, June 2004. © IEEE, [ps][pdf][slides] |
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| The Kullback-Leibler Kernel as a Framework for Discriminant and Localized Representations for Visual Recognition N. Vasconcelos, P. Ho, and P. Moreno, Proceedings of the European Conference on Computer Vision, Prague, Czech, May 2004.[ps][pdf][slides] |
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Formulating Semantic Image Annotation as a Supervised Learning Problem |
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Efficient Computation of the KL Divergence between Dynamic Textures |
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A Family of Probabilistic Kernels Based on Information Divergence |
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| 2003 | |
| A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications P. J. Moreno, P. P. Ho, and N. Vasconcelos Proceedings of Neural Information Processing Systems, Vancouver, Canada, December 2003. [ps][pdf]. |
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| Feature Selection by Maximum Marginal Diversity: optimality and implications for visual recognition N. Vasconcelos Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Madison, Wisconsin, June 2003, © IEEE, [ps][pdf] |
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| The design of end-to-end optimal image retrieval systems N. Vasconcelos Proceedings of International Conference on Artificial Neural Networks, Istanbul, Turkey, June 2003. [ps][pdf] |
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| 2002 | |
| Feature Selection by Maximum Marginal Diversity N. Vasconcelos Proceedings of Neural Information Processing Systems, Vancouver, Canada, December 2002. [ps][pdf]. |
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| Exploiting Group Structure To Improve Retrieval Accuracy and Speed in Image Databases N. Vasconcelos, Proceedings of International Conference on Image Processing, Rochester, New York, September 2002, © IEEE, [ps][pdf]. |
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| What is the Role of Independence for Visual Recognition? N. Vasconcelos and G. Carneiro, Proceedings of the European Conference on Computer Vision, Copenhagen, Denmark, May 2002.[ps][pdf] |
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| 2001 | |
| Empirical Bayesian Motion Segmentation N. Vasconcelos and A. Lippman, IEEE Transactions on Pattern Analysis and Machine Inteligence, vol.23, n.2; February 2001, © IEEE, [ps][pdf]. |
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| Content-based Retrieval from Image Databases: Current Solutions and Future Directions N. Vasconcelos and M. Kunt, Proceedings of International Conference on Image Processing, Thessaloniki, Greece, October 2001, © IEEE, [ps][pdf]. |
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| On the Complexity of Probabilistic Image Retrieval N. Vasconcelos, Proceedings of the International Conference on Computer Vision, Vancouver, Canada, July 2001, © IEEE, [ps][pdf] |
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| Image Indexing with Mixture Hierarchies N. Vasconcelos Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawai, June 2001, © IEEE, [ps][pdf] |
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| 2000 | |
| Statistical Models of Video Structure for Content Analysis and Characterization N. Vasconcelos and A. Lippman, IEEE Transactions on Image Processing, vol. 9, n. 1; January 2000, © IEEE, [ps][pdf]. |
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| Bayesian Video Shot Segmentation N. Vasconcelos and A. Lippman, Proceedings of Neural Information Processing Systems 13, Denver, Colorado, December 2000, [ps][pdf]. |
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| A Unifying View of Image Similarity N. Vasconcelos and A. Lippman, Proceedings of the International Conference on Pattern Recognition, Barcelona, Spain, September 2000, © IEEE, [ps][pdf]. |
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| Feature Representations for Image Retrieval: Beyond the Color Histogram N. Vasconcelos and A. Lippman, Proceedings of the International Conference on Multimedia and Expo, New York, August 2000, © IEEE, [ps][pdf]. |
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| Learning Over Multiple Temporal Scales in Image Databases N. Vasconcelos and A. Lippman, Proceedings of the European Conference on Computer Vision, Dublin, Ireland, July 2000, ? Springer, [ps][pdf]. |
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| A Probabilistic Architecture for Content-based Image Retrieval N. Vasconcelos and A. Lippman, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, South Carolina, June 2000, © IEEE, [ps][pdf]. |
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| Bayesian Representations and Learning Mechanisms for Content Based Image Retrieval N. Vasconcelos and A. Lippman, Proceedings SPIE Conference on Storage and Retrieval for Media Databases, San Jose, California, January 2000. |
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| 1999 | |
| Learning from User Feedback in Image Retrieval Systems N. Vasconcelos and A. Lippman, Proceedings of Neural Information Processing Systems 12, Denver, Colorado, December 1999, [ps][pdf]. |
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| 1998 | |
| Learning Mixture Hierarchies N. Vasconcelos and A. Lippman, Proceedings of Neural Information Processing Systems 11, Denver, Colorado, December 1998, [ps][pdf]. |
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| Humane Interfaces to Video A. Lippman, N. Vasconcelos, and G. Iyengar, Proceedings of 32nd Asilomar Conference on Signals, Systems, and Computers, Asilomar, California, November 1998. |
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Embedded Mixture Modeling for Efficient Probabilistic Content-Based Indexing and Retrieval |
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| Bayesian Modeling of Video Editing and Structure: Semantic Features for Video Summarization and Browsing N. Vasconcelos and A. Lippman, Proceedings of International Conference on Image Processing, Chicago, October 1998, © IEEE, [ps][pdf]. |
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| A Spatiotemporal Motion Model for Video Summarization N. Vasconcelos and A. Lippman, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, June 1998, © IEEE, [ps][pdf] |
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| A Bayesian Framework for Semantic Content Characterization N. Vasconcelos and A. Lippman, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, June 1998, © IEEE, [ps][pdf] |
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| A Bayesian Framework for Content-based Indexing and Retrieval N. Vasconcelos and A. Lippman, Proceedings of IEEE Data Compression Conference Snowbird, Utah, March 1998, © IEEE, [ps][pdf] |
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| 1997 | |
| Multiresolution Tangent Distance for Affine Invariant Classification N. Vasconcelos and A. Lippman, Proceedings of Neural Information Processing Systems 10, Denver, Colorado, December 1997, [ps][pdf]. |
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| Content-based Pre-indexed Video N. Vasconcelos and A. Lippman, Proceedings of International Conference on Image Processing, Santa Barbara, California, October 1997, © IEEE, [ps][pdf]. |
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| Pre and Post-Filtering for Low Bit-rate Video Coding N. Vasconcelos and A. Lippman, Proceedings of International Conference on Image Processing, Santa Barbara, California, October 1997. |
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| Towards Semantically Meaningful Feature Spaces for the Characterization of Video Content N. Vasconcelos and A. Lippman, Proceedings of International Conference on Image Processing, Santa Barbara, California, October 1997, © IEEE, [ps][pdf]. |
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| Empirical Bayesian EM-based Motion Segmentation N. Vasconcelos and A. Lippman, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, June 1997, © IEEE, [ps][pdf]. |
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| Library-based Coding: a Representation for Efficient Video Compression and Retrieval N. Vasconcelos and A. Lippman, Proceedings of IEEE Data Compression Conference, Snowbird, Utah, March 1997, © IEEE, [ps][pdf]. |
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| 1996 | |
| Frame-free Video N. Vasconcelos and A. Lippman, Proceedings of International Conference on Image Processing, Lausanne, Switzerland, September 1996, © IEEE, [ps][pdf]. |
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| 1995 | |
| Spatiotemporal Model-Based Optic Flow Estimation N. Vasconcelos and A. Lippman, Proceedings of International Conference on Image Processing, Washington DC, October 1995, © IEEE, [ps][pdf]. |
Copyright @ 2007 www.svcl.ucsd.edu