Semantic Information Pursuit for Multimodal Data Analysis

Lead Research Organisation: University of Oxford
Department Name: Statistics

Abstract

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Publications

10 25 50
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Zhu X (2021) Complementary Discriminative Correlation Filters Based on Collaborative Representation for Visual Object Tracking in IEEE Transactions on Circuits and Systems for Video Technology

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Zantedeschi V. (2021) Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound in Advances in Neural Information Processing Systems

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Xu T (2020) An accelerated correlation filter tracker in Pattern Recognition

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Tadic V (2020) Stability of optimal filter higher-order derivatives in Stochastic Processes and their Applications

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Tadic V (2019) Analyticity of Entropy Rates of Continuous-State Hidden Markov Models in IEEE Transactions on Information Theory

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Tadic V (2021) Asymptotic Properties of Recursive Particle Maximum Likelihood Estimation in IEEE Transactions on Information Theory

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Rivasplata O. (2020) PAC-Bayes analysis beyond the usual bounds in Advances in Neural Information Processing Systems

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PĂ©rez-Ortiz M. (2021) Tighter risk certificates for neural networks in Journal of Machine Learning Research

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Phillips A (2022) Spectral Diffusion Processes

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Mikhailiuk A (2022) Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality in IEEE Transactions on Multimedia

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Middleton L (2020) Unbiased Markov chain Monte Carlo for intractable target distributions in Electronic Journal of Statistics

 
Title Estimation of Copulas via Maximum Mean Discrepancy 
Description This article deals with robust inference for parametric copula models. Estimation using canonical maximum likelihood might be unstable, especially in the presence of outliers. We propose to use a procedure based on the maximum mean discrepancy (MMD) principle. We derive nonasymptotic oracle inequalities, consistency and asymptotic normality of this new estimator. In particular, the oracle inequality holds without any assumption on the copula family, and can be applied in the presence of outliers or under misspecification. Moreover, in our MMD framework, the statistical inference of copula models for which there exists no density with respect to the Lebesgue measure on [0,1]d, as the Marshall-Olkin copula, becomes feasible. A simulation study shows the robustness of our new procedures, especially compared to pseudo-maximum likelihood estimation. An R package implementing the MMD estimator for copula models is available. Supplementary materials for this article are available online. 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL https://tandf.figshare.com/articles/dataset/Estimation_of_Copulas_via_Maximum_Mean_Discrepancy/19487...
 
Title Estimation of Copulas via Maximum Mean Discrepancy 
Description This article deals with robust inference for parametric copula models. Estimation using canonical maximum likelihood might be unstable, especially in the presence of outliers. We propose to use a procedure based on the maximum mean discrepancy (MMD) principle. We derive nonasymptotic oracle inequalities, consistency and asymptotic normality of this new estimator. In particular, the oracle inequality holds without any assumption on the copula family, and can be applied in the presence of outliers or under misspecification. Moreover, in our MMD framework, the statistical inference of copula models for which there exists no density with respect to the Lebesgue measure on [0,1]d, as the Marshall-Olkin copula, becomes feasible. A simulation study shows the robustness of our new procedures, especially compared to pseudo-maximum likelihood estimation. An R package implementing the MMD estimator for copula models is available. Supplementary materials for this article are available online. 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL https://tandf.figshare.com/articles/dataset/Estimation_of_Copulas_via_Maximum_Mean_Discrepancy/19487...
 
Title UPIQ: Unified Photometric Image Quality dataset 
Description Unified Photometric Image Quality dataset (UPIQ) UPIQ dataset is intended for training and evaluation of full-reference HDR image quality metrics. The dataset contains 84 reference images and 4159 distorted images from four datasets, TID2013 [1] (SDR), LIVE [2] (SDR), Narwaria et al. [3] (HDR) and Korshunov et al. [4] (HDR). Quality scores were obtained by re-aligning existing datasets to a common unified quality scale. This was achieved by collecting additional cross-dataset quality comparisons and re-scaling existing data with a psychometric scaling method. Images in the dataset are represented in absolute photometric and colorimetric units, corresponding to light emitted from a display. [1] Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J., Benoit: Image database tid2013: Peculiarities, results and perspectives. Signal Processing: Image Communication 30, 57 - 77 (2015) [2] Sheikh, H., Sabir, M., Bovik, A.: A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms. IEEE Transactions on Image Processing 15(11), 3440-3451 (2006). https://doi.org/10.1109/TIP.2006.881959, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1709988 [3] Narwaria, M., P. Da Silva, M., Le Callet, P., Pepion, R.: Tone mapping-based high-dynamic-range image compression: study of optimization criterion and perceptual quality. Optical Engineering 52(10) (2013). https://doi.org/10.1117/1.OE.52.10.102008 [4] Korshunov, P., Hanhart, P., Richter, T., Artusi, A., Mantiuk, R., Ebrahimi, T.: Subjective quality assessment database of HDR images compressed with jpeg xt. In: 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX). pp. 1-6 (May 2015). https://doi.org/10.1109/QoMEX.2015.7148119 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
URL https://www.repository.cam.ac.uk/handle/1810/315373
 
Title UPIQ: Unified Photometric Image Quality dataset (04.2021) 
Description Unified Photometric Image Quality dataset (UPIQ) UPIQ dataset is intended for training and evaluation of full-reference HDR image quality metrics. The dataset contains 84 reference images and 4159 distorted images from four datasets, TID2013 [1] (SDR), LIVE [2] (SDR), Narwaria et al. [3] (HDR) and Korshunov et al. [4] (HDR). Quality scores were obtained by re-aligning existing datasets to a common unified quality scale. This was achieved by collecting additional cross-dataset quality comparisons and re-scaling existing data with a psychometric scaling method. Images in the dataset are represented in absolute photometric and colorimetric units, corresponding to light emitted from a display. This is an updated version of the dataset with the fixed pix_per_deg column. See README.md. [1] Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J., Benoit: Image database tid2013: Peculiarities, results and perspectives. Signal Processing: Image Communication 30, 57 - 77 (2015) [2] Sheikh, H., Sabir, M., Bovik, A.: A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms. IEEE Transactions on Image Processing 15(11), 3440-3451 (2006). https://doi.org/10.1109/TIP.2006.881959, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1709988 [3] Narwaria, M., P. Da Silva, M., Le Callet, P., Pepion, R.: Tone mapping-based high-dynamic-range image compression: study of optimization criterion and perceptual quality. Optical Engineering 52(10) (2013). https://doi.org/10.1117/1.OE.52.10.102008 [4] Korshunov, P., Hanhart, P., Richter, T., Artusi, A., Mantiuk, R., Ebrahimi, T.: Subjective quality assessment database of HDR images compressed with jpeg xt. In: 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX). pp. 1-6 (May 2015). https://doi.org/10.1109/QoMEX.2015.7148119 
Type Of Material Database/Collection of data 
Year Produced 2021 
Provided To Others? Yes  
URL https://www.repository.cam.ac.uk/handle/1810/321331