The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
E. Alpaydm - One of the best experts on this subject based on the ideXlab platform.
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Multiclass Posterior Probability Support Vector Machines
IEEE transactions on neural networks, 2008Co-Authors: Mehmet Gönen, A.g. Tanugur, E. AlpaydmAbstract:Tao et. al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be more robust to noise and outliers. Tao et. al.'s model uses a window-based density estimator to calculate the posterior probabilities and is a binary classifier. We propose a neighbor-based density estimator and also extend the model to the Multiclass Case. Our bias-variance analysis shows that the decrease in error by PPSVM is due to a decrease in bias. On 20 benchmark data sets, we observe that PPSVM obtains accuracy results that are higher or comparable to those of canonical SVM using significantly fewer support vectors.
Mehmet Gönen - One of the best experts on this subject based on the ideXlab platform.
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Multiclass Posterior Probability Support Vector Machines
IEEE transactions on neural networks, 2008Co-Authors: Mehmet Gönen, A.g. Tanugur, E. AlpaydmAbstract:Tao et. al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be more robust to noise and outliers. Tao et. al.'s model uses a window-based density estimator to calculate the posterior probabilities and is a binary classifier. We propose a neighbor-based density estimator and also extend the model to the Multiclass Case. Our bias-variance analysis shows that the decrease in error by PPSVM is due to a decrease in bias. On 20 benchmark data sets, we observe that PPSVM obtains accuracy results that are higher or comparable to those of canonical SVM using significantly fewer support vectors.
Alvaro Soto - One of the best experts on this subject based on the ideXlab platform.
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Learning Shared, Discriminative, and Compact Representations for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015Co-Authors: Hans Lobel, René Vidal, Alvaro SotoAbstract:Dictionary-based and part-based methods are among the most popular approaches to visual recognition. In both methods, a mid-level representation is built on top of low-level image descriptors and high-level classifiers are trained on top of the mid-level representation. While earlier methods built the mid-level representation without supervision, there is currently great interest in learning both representations jointly to make the mid-level representation more discriminative. In this work we propose a new approach to visual recognition that jointly learns a shared, discriminative, and compact mid-level representation and a compact high-level representation. By using a structured output learning framework, our approach directly handles the Multiclass Case at both levels of abstraction. Moreover, by using a group-sparse prior in the structured output learning framework, our approach encourages sharing of visual words and thus reduces the number of words used to represent each class. We test our proposed method on several popular benchmarks. Our results show that, by jointly learning midand high-level representations, and fostering the sharing of discriminative visual words among target classes, we are able to achieve state-of-the-art recognition performance using far less visual words than previous approaches.
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ICCV - Hierarchical Joint Max-Margin Learning of Mid and Top Level Representations for Visual Recognition
2013 IEEE International Conference on Computer Vision, 2013Co-Authors: Hans Lobel, René Vidal, Alvaro SotoAbstract:Currently, Bag-of-Visual-Words (BoVW) and part-based methods are the most popular approaches for visual recognition. In both Cases, a mid-level representation is built on top of low-level image descriptors and top-level classifiers use this mid-level representation to achieve visual recognition. While in current part-based approaches, mid- and top-level representations are usually jointly trained, this is not the usual Case for BoVW schemes. A main reason for this is the complex data association problem related to the usual large dictionary size needed by BoVW approaches. As a further observation, typical solutions based on BoVW and part-based representations are usually limited to extensions of binary classification schemes, a strategy that ignores relevant correlations among classes. In this work we propose a novel hierarchical approach to visual recognition based on a BoVW scheme that jointly learns suitable mid- and top-level representations. Furthermore, using a max-margin learning framework, the proposed approach directly handles the Multiclass Case at both levels of abstraction. We test our proposed method using several popular benchmark datasets. As our main result, we demonstrate that, by coupling learning of mid- and top-level representations, the proposed approach fosters sharing of discriminative visual words among target classes, being able to achieve state-of-the-art recognition performance using far less visual words than previous approaches.
A.g. Tanugur - One of the best experts on this subject based on the ideXlab platform.
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Multiclass Posterior Probability Support Vector Machines
IEEE transactions on neural networks, 2008Co-Authors: Mehmet Gönen, A.g. Tanugur, E. AlpaydmAbstract:Tao et. al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be more robust to noise and outliers. Tao et. al.'s model uses a window-based density estimator to calculate the posterior probabilities and is a binary classifier. We propose a neighbor-based density estimator and also extend the model to the Multiclass Case. Our bias-variance analysis shows that the decrease in error by PPSVM is due to a decrease in bias. On 20 benchmark data sets, we observe that PPSVM obtains accuracy results that are higher or comparable to those of canonical SVM using significantly fewer support vectors.
Hans Lobel - One of the best experts on this subject based on the ideXlab platform.
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Learning Shared, Discriminative, and Compact Representations for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015Co-Authors: Hans Lobel, René Vidal, Alvaro SotoAbstract:Dictionary-based and part-based methods are among the most popular approaches to visual recognition. In both methods, a mid-level representation is built on top of low-level image descriptors and high-level classifiers are trained on top of the mid-level representation. While earlier methods built the mid-level representation without supervision, there is currently great interest in learning both representations jointly to make the mid-level representation more discriminative. In this work we propose a new approach to visual recognition that jointly learns a shared, discriminative, and compact mid-level representation and a compact high-level representation. By using a structured output learning framework, our approach directly handles the Multiclass Case at both levels of abstraction. Moreover, by using a group-sparse prior in the structured output learning framework, our approach encourages sharing of visual words and thus reduces the number of words used to represent each class. We test our proposed method on several popular benchmarks. Our results show that, by jointly learning midand high-level representations, and fostering the sharing of discriminative visual words among target classes, we are able to achieve state-of-the-art recognition performance using far less visual words than previous approaches.
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ICCV - Hierarchical Joint Max-Margin Learning of Mid and Top Level Representations for Visual Recognition
2013 IEEE International Conference on Computer Vision, 2013Co-Authors: Hans Lobel, René Vidal, Alvaro SotoAbstract:Currently, Bag-of-Visual-Words (BoVW) and part-based methods are the most popular approaches for visual recognition. In both Cases, a mid-level representation is built on top of low-level image descriptors and top-level classifiers use this mid-level representation to achieve visual recognition. While in current part-based approaches, mid- and top-level representations are usually jointly trained, this is not the usual Case for BoVW schemes. A main reason for this is the complex data association problem related to the usual large dictionary size needed by BoVW approaches. As a further observation, typical solutions based on BoVW and part-based representations are usually limited to extensions of binary classification schemes, a strategy that ignores relevant correlations among classes. In this work we propose a novel hierarchical approach to visual recognition based on a BoVW scheme that jointly learns suitable mid- and top-level representations. Furthermore, using a max-margin learning framework, the proposed approach directly handles the Multiclass Case at both levels of abstraction. We test our proposed method using several popular benchmark datasets. As our main result, we demonstrate that, by coupling learning of mid- and top-level representations, the proposed approach fosters sharing of discriminative visual words among target classes, being able to achieve state-of-the-art recognition performance using far less visual words than previous approaches.