The Experts below are selected from a list of 47115 Experts worldwide ranked by ideXlab platform
Wenrui Dai - One of the best experts on this subject based on the ideXlab platform.
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Large Discriminative Structured Set Prediction Modeling With Max-Margin Markov Network for Lossless Image Coding
IEEE Transactions on Image Processing, 2014Co-Authors: Wenrui Dai, Hongkai Xiong, Jia Wang, Yuan F. ZhengAbstract:Inherent statistical correlation for context-based prediction and structural interdependencies for local coherence is not fully exploited in existing lossless image coding schemes. This paper proposes a novel prediction model where the optimal correlated prediction for a set of pixels is obtained in the sense of the least code length. It not only exploits the spatial statistical correlations for the optimal prediction directly based on 2D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Under the joint constraints for local coherence, max-margin Markov Networks are incorporated to combine support vector machines structurally to make max-margin estimation for a correlated region. Specifically, it aims to produce multiple predictions in the blocks with the model parameters learned in such a way that the distinction between the actual pixel and all possible estimations is maximized. It is proved that, with the growth of sample size, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. Incorporated into the lossless image coding framework, the proposed model outperforms most prediction schemes reported.
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Structured set intra prediction with discriminative learning in a max-margin Markov Network for high efficiency video coding
IEEE Transactions on Circuits and Systems for Video Technology, 2013Co-Authors: Wenrui Dai, Xiaoqian Jiang, Hongkai Xiong, Chang Wen ChenAbstract:This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov Network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov Network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding.
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optimal intra coding of hevc by structured set prediction mode with discriminative learning
Visual Communications and Image Processing, 2012Co-Authors: Wenrui Dai, Hongkai XiongAbstract:This paper proposes a novel model on intra-coding for high efficiency video coding (HEVC), which can simultaneously make the set of prediction for block of pixels in an optimal rate-distortion sense. It not only utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the probability distribution which is favorable for subsequent transform and coding. The so-called structured set prediction model incorporates max-margin Markov Network to regulate and reason the multiple prediction in the blocks. The model parameters are learned by discriminating the actual pixel value from the other possible estimates to the maximal margin. Distinguished from the existing methods concerning the minimal prediction error, the Markov Network is adaptively derived to maintain the coherence of set of prediction. To be concrete, the proposed model seeks the concurrent optimization of the set of prediction by relating the loss function to the probability distribution of subsequent DCT coefficients. The prediction error is demonstrated to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we integrate the proposed model into HEVC intra coding and experimental results show obvious improvement of coding performance in terms of BD-rate.
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discriminative structured set prediction modeling with max margin Markov Network for optimal lossless image coding
Visual Communications and Image Processing, 2012Co-Authors: Wenrui Dai, Hongkai XiongAbstract:In this paper, we investigate and propose a novel prediction model for lossless image coding in which the optimal correlated prediction for block of pixels are simultaneously obtained in the sense of the least code length. It not only utilizes the spatial statistical correlation for the optimal prediction directly based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Besides the discriminative adaptive pixel-wise prediction, the Markov Network is adaptively derived to maintain the coherence of prediction in the blocks and seek the concurrent optimization of set of prediction by relating the loss function to actual code length. The prediction error is shown to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we apply the proposed model into lossless image coding and experimental results show that the proposed scheme outperforms the best prediction scheme reported.
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VCIP - Optimal intra coding of HEVC by structured set prediction mode with discriminative learning
2012 Visual Communications and Image Processing, 2012Co-Authors: Wenrui Dai, Hongkai XiongAbstract:This paper proposes a novel model on intra-coding for high efficiency video coding (HEVC), which can simultaneously make the set of prediction for block of pixels in an optimal rate-distortion sense. It not only utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the probability distribution which is favorable for subsequent transform and coding. The so-called structured set prediction model incorporates max-margin Markov Network to regulate and reason the multiple prediction in the blocks. The model parameters are learned by discriminating the actual pixel value from the other possible estimates to the maximal margin. Distinguished from the existing methods concerning the minimal prediction error, the Markov Network is adaptively derived to maintain the coherence of set of prediction. To be concrete, the proposed model seeks the concurrent optimization of the set of prediction by relating the loss function to the probability distribution of subsequent DCT coefficients. The prediction error is demonstrated to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we integrate the proposed model into HEVC intra coding and experimental results show obvious improvement of coding performance in terms of BD-rate.
Hongkai Xiong - One of the best experts on this subject based on the ideXlab platform.
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Large Discriminative Structured Set Prediction Modeling With Max-Margin Markov Network for Lossless Image Coding
IEEE Transactions on Image Processing, 2014Co-Authors: Wenrui Dai, Hongkai Xiong, Jia Wang, Yuan F. ZhengAbstract:Inherent statistical correlation for context-based prediction and structural interdependencies for local coherence is not fully exploited in existing lossless image coding schemes. This paper proposes a novel prediction model where the optimal correlated prediction for a set of pixels is obtained in the sense of the least code length. It not only exploits the spatial statistical correlations for the optimal prediction directly based on 2D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Under the joint constraints for local coherence, max-margin Markov Networks are incorporated to combine support vector machines structurally to make max-margin estimation for a correlated region. Specifically, it aims to produce multiple predictions in the blocks with the model parameters learned in such a way that the distinction between the actual pixel and all possible estimations is maximized. It is proved that, with the growth of sample size, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. Incorporated into the lossless image coding framework, the proposed model outperforms most prediction schemes reported.
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Structured set intra prediction with discriminative learning in a max-margin Markov Network for high efficiency video coding
IEEE Transactions on Circuits and Systems for Video Technology, 2013Co-Authors: Wenrui Dai, Xiaoqian Jiang, Hongkai Xiong, Chang Wen ChenAbstract:This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov Network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov Network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding.
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optimal intra coding of hevc by structured set prediction mode with discriminative learning
Visual Communications and Image Processing, 2012Co-Authors: Wenrui Dai, Hongkai XiongAbstract:This paper proposes a novel model on intra-coding for high efficiency video coding (HEVC), which can simultaneously make the set of prediction for block of pixels in an optimal rate-distortion sense. It not only utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the probability distribution which is favorable for subsequent transform and coding. The so-called structured set prediction model incorporates max-margin Markov Network to regulate and reason the multiple prediction in the blocks. The model parameters are learned by discriminating the actual pixel value from the other possible estimates to the maximal margin. Distinguished from the existing methods concerning the minimal prediction error, the Markov Network is adaptively derived to maintain the coherence of set of prediction. To be concrete, the proposed model seeks the concurrent optimization of the set of prediction by relating the loss function to the probability distribution of subsequent DCT coefficients. The prediction error is demonstrated to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we integrate the proposed model into HEVC intra coding and experimental results show obvious improvement of coding performance in terms of BD-rate.
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discriminative structured set prediction modeling with max margin Markov Network for optimal lossless image coding
Visual Communications and Image Processing, 2012Co-Authors: Wenrui Dai, Hongkai XiongAbstract:In this paper, we investigate and propose a novel prediction model for lossless image coding in which the optimal correlated prediction for block of pixels are simultaneously obtained in the sense of the least code length. It not only utilizes the spatial statistical correlation for the optimal prediction directly based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Besides the discriminative adaptive pixel-wise prediction, the Markov Network is adaptively derived to maintain the coherence of prediction in the blocks and seek the concurrent optimization of set of prediction by relating the loss function to actual code length. The prediction error is shown to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we apply the proposed model into lossless image coding and experimental results show that the proposed scheme outperforms the best prediction scheme reported.
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VCIP - Optimal intra coding of HEVC by structured set prediction mode with discriminative learning
2012 Visual Communications and Image Processing, 2012Co-Authors: Wenrui Dai, Hongkai XiongAbstract:This paper proposes a novel model on intra-coding for high efficiency video coding (HEVC), which can simultaneously make the set of prediction for block of pixels in an optimal rate-distortion sense. It not only utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the probability distribution which is favorable for subsequent transform and coding. The so-called structured set prediction model incorporates max-margin Markov Network to regulate and reason the multiple prediction in the blocks. The model parameters are learned by discriminating the actual pixel value from the other possible estimates to the maximal margin. Distinguished from the existing methods concerning the minimal prediction error, the Markov Network is adaptively derived to maintain the coherence of set of prediction. To be concrete, the proposed model seeks the concurrent optimization of the set of prediction by relating the loss function to the probability distribution of subsequent DCT coefficients. The prediction error is demonstrated to be asymptotically upper bounded by the training error under the decomposable loss function. For validation, we integrate the proposed model into HEVC intra coding and experimental results show obvious improvement of coding performance in terms of BD-rate.
Masashi Sugiyama - One of the best experts on this subject based on the ideXlab platform.
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Direct learning of sparse changes in Markov Networks by density ratio estimation
Neural computation, 2014Co-Authors: Song Liu, John Quinn, Michael U. Gutmann, Taiji Suzuki, Masashi SugiyamaAbstract:We propose a new method for detecting changes in Markov Network structure between two sets of samples. Instead of naively fitting two Markov Network models separately to the two data sets and figuring out their difference, we directly learn the Network structure change by estimating the ratio of Markov Network models. This density-ratio formulation naturally allows us to introduce sparsity in the Network structure change, which highly contributes to enhancing interpretability. Furthermore, computation of the normalization term, a critical bottleneck of the naive approach, can be remarkably mitigated. We also give the dual formulation of the optimization problem, which further reduces the computation cost for large-scale Markov Networks. Through experiments, we demonstrate the usefulness of our method.
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Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
arXiv: Machine Learning, 2013Co-Authors: Song Liu, John Quinn, Michael U. Gutmann, Taiji Suzuki, Masashi SugiyamaAbstract:We propose a new method for detecting changes in Markov Network structure between two sets of samples. Instead of naively fitting two Markov Network models separately to the two data sets and figuring out their difference, we \emph{directly} learn the Network structure change by estimating the ratio of Markov Network models. This density-ratio formulation naturally allows us to introduce sparsity in the Network structure change, which highly contributes to enhancing interpretability. Furthermore, computation of the normalization term, which is a critical bottleneck of the naive approach, can be remarkably mitigated. We also give the dual formulation of the optimization problem, which further reduces the computation cost for large-scale Markov Networks. Through experiments, we demonstrate the usefulness of our method.
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ECML/PKDD (2) - Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
Advanced Information Systems Engineering, 2013Co-Authors: Song Liu, John Quinn, Michael U. Gutmann, Masashi SugiyamaAbstract:We propose a new method for detecting changes in Markov Network structure between two sets of samples. Instead of naively fitting two Markov Network models separately to the two data sets and figuring out their difference, we directly learn the Network structure change by estimating the ratio of Markov Network models. This density-ratio formulation naturally allows us to introduce sparsity in the Network structure change, which highly contributes to enhancing interpretability. Furthermore, computation of the normalization term, which is a critical computational bottleneck of the naive approach, can be remarkably mitigated. Through experiments on gene expression and Twitter data analysis, we demonstrate the usefulness of our method.
Facundo Bromberg - One of the best experts on this subject based on the ideXlab platform.
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The IBMAP approach for Markov Network structure learning
Annals of Mathematics and Artificial Intelligence, 2014Co-Authors: Federico Schlüter, Facundo Bromberg, Alejandro EderaAbstract:In this work we consider the problem of learning the structure of Markov Networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum.
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independence based map for Markov Networks structure discovery
International Conference on Tools with Artificial Intelligence, 2011Co-Authors: Facundo Bromberg, Federico Schlüter, Alejandro EderaAbstract:This work presents IBMAP, an approach for robust learning of Markov Network structures from data, together with IBMAP-HC, an efficient instantiation of the approach. Existing Score-Based (SB) and Independence-Based (IB) approaches must make concessions either on robustness or efficiency. IBMAP-HC improves robustness efficiently through an IB-SB hybrid approach based on the probabilistic Maximum-A-Posteriori (MAP) technique, and the IB-score, a tractable expression for computing posterior probabilities of Markov Network structures. Performance is first tested against IB and SB competitors on synthetic datasets. Against IB competitors (GSMN algorithm and a version of the HHC algorithm adapted here for Markov Networks discovery), IBMAP-HC showed reductions in edges Hamming distance with same order running times. Against SB competitors, both IBMAP-HC and our adaptation of HHC produced comparable Hamming distances, but with running times orders of magnitude faster. We also evaluated IBMAP-HC in a realistic, challenging test-bed: EDAs, evolutionary algorithms for optimization that estimate a distribution on each generation. Using IBMAP-HC to estimate distributions, EDAs converged to the optimum faster in all benchmark functions considered, reducing required fitness evaluations by up to 80%.
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ICTAI - Independence-Based MAP for Markov Networks Structure Discovery
2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011Co-Authors: Facundo Bromberg, Federico Schlüter, Alejandro EderaAbstract:This work presents IBMAP, an approach for robust learning of Markov Network structures from data, together with IBMAP-HC, an efficient instantiation of the approach. Existing Score-Based (SB) and Independence-Based (IB) approaches must make concessions either on robustness or efficiency. IBMAP-HC improves robustness efficiently through an IB-SB hybrid approach based on the probabilistic Maximum-A-Posteriori (MAP) technique, and the IB-score, a tractable expression for computing posterior probabilities of Markov Network structures. Performance is first tested against IB and SB competitors on synthetic datasets. Against IB competitors (GSMN algorithm and a version of the HHC algorithm adapted here for Markov Networks discovery), IBMAP-HC showed reductions in edges Hamming distance with same order running times. Against SB competitors, both IBMAP-HC and our adaptation of HHC produced comparable Hamming distances, but with running times orders of magnitude faster. We also evaluated IBMAP-HC in a realistic, challenging test-bed: EDAs, evolutionary algorithms for optimization that estimate a distribution on each generation. Using IBMAP-HC to estimate distributions, EDAs converged to the optimum faster in all benchmark functions considered, reducing required fitness evaluations by up to 80%.
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efficient Markov Network structure discovery using independence tests
Journal of Artificial Intelligence Research, 2009Co-Authors: Facundo Bromberg, Dimitris Margaritis, Vasant HonavarAbstract:We present two algorithms for learning the structure of a Markov Network from data: GSMN* and GSIMN. Both algorithms use statistical independence tests to infer the structure by successively constraining the set of structures consistent with the results of these tests. Until very recently, algorithms for structure learning were based on maximum likelihood estimation, which has been proved to be NP-hard for Markov Networks due to the difficulty of estimating the parameters of the Network, needed for the computation of the data likelihood. The independence-based approach does not require the computation of the likelihood, and thus both GSMN* and GSIMN can compute the structure efficiently (as shown in our experiments). GSMN* is an adaptation of the Grow-Shrink algorithm of Margaritis and Thrun for learning the structure of Bayesian Networks. GSIMN extends GSMN* by additionally exploiting Pearl's well-known properties of the conditional independence relation to infer novel independences from known ones, thus avoiding the performance of statistical tests to estimate them. To accomplish this efficiently GSIMN uses the Triangle theorem, also introduced in this work, which is a simplified version of the set of Markov axioms. Experimental comparisons on artificial and real-world data sets show GSIMN can yield significant savings with respect to GSMN*, while generating a Markov Network with comparable or in some cases improved quality. We also compare GSIMN to a forward-chaining implementation, called GSIMN-FCH, that produces all possible conditional independences resulting from repeatedly applying Pearl's theorems on the known conditional independence tests. The results of this comparison show that GSIMN, by the sole use of the Triangle theorem, is nearly optimal in terms of the set of independences tests that it infers.
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efficient and robust independence based Markov Network structure discovery
International Joint Conference on Artificial Intelligence, 2007Co-Authors: Facundo Bromberg, Dimitris MargaritisAbstract:In this paper we introduce a novel algorithm for the induction of the Markov Network structure of a domain from the outcome of conditional independence tests on data. Such algorithms work by successively restricting the set of possible structures until there is only a single structure consistent with the conditional independence tests executed. Existing independence-based algorithms have wellknown shortcomings, such as rigidly ordering the sequence of tests they perform, resulting in potential inefficiencies in the number of tests required, and committing fully to the test outcomes, resulting in lack of robustness in case of unreliable tests. We address both problems through a Bayesian particle filtering approach, which uses a population of Markov Network structures to maintain the posterior probability distribution over them, given the outcomes of the tests performed. Instead of a fixed ordering, our approach greedily selects, at each step, the optimally informative from a pool of candidate tests according to information gain. In addition, it maintains multiple candidate structures weighed by posterior probability, which makes it more robust to errors in the test outcomes. The result is an approximate algorithm (due to the use of particle filtering) that is useful in domains where independence tests are uncertain (such as applications where little data is available) or expensive (such as cases of very large data sets and/or distributed data).
Yuan F. Zheng - One of the best experts on this subject based on the ideXlab platform.
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Large Discriminative Structured Set Prediction Modeling With Max-Margin Markov Network for Lossless Image Coding
IEEE Transactions on Image Processing, 2014Co-Authors: Wenrui Dai, Hongkai Xiong, Jia Wang, Yuan F. ZhengAbstract:Inherent statistical correlation for context-based prediction and structural interdependencies for local coherence is not fully exploited in existing lossless image coding schemes. This paper proposes a novel prediction model where the optimal correlated prediction for a set of pixels is obtained in the sense of the least code length. It not only exploits the spatial statistical correlations for the optimal prediction directly based on 2D contexts, but also formulates the data-driven structural interdependencies to make the prediction error coherent with the underlying probability distribution for coding. Under the joint constraints for local coherence, max-margin Markov Networks are incorporated to combine support vector machines structurally to make max-margin estimation for a correlated region. Specifically, it aims to produce multiple predictions in the blocks with the model parameters learned in such a way that the distinction between the actual pixel and all possible estimations is maximized. It is proved that, with the growth of sample size, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. Incorporated into the lossless image coding framework, the proposed model outperforms most prediction schemes reported.