The Experts below are selected from a list of 42 Experts worldwide ranked by ideXlab platform
Philippe-henri Gosselin - One of the best experts on this subject based on the ideXlab platform.
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ICCV Workshops - A Handcrafted Normalized-Convolution Network for Texture Classification
2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017Co-Authors: Ngoc-son Vu, Vu-lam Nguyen, Philippe-henri GosselinAbstract:In this paper, we propose a Handcrafted Normalized-Convolution Network (NmzNet) for efficient texture classification. NmzNet is implemented by a three-layer normalized convolution network, which computes successive normalized convolution with a Predefined Filter bank (Gabor Filter bank) and modulus non-linearities. Coefficients from different layers are aggregated by Fisher Vector aggregation to form the final discriminative features. The results of experimental evaluation on three texture datasets UIUC, KTH-TIPS-2a, and KTH-TIPS-2b indicate that our proposed approach achieves the good classification rate compared with other handcrafted methods. The results additionally indicate that only a marginal difference exists between the best classification rate of recent frontiers CNN and that of the proposed method on the experimented datasets.
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A Handcrafted Normalized-Convolution Network for Texture Classification
2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017Co-Authors: Ngoc-son Vu, Vu-lam Nguyen, Philippe-henri GosselinAbstract:In this paper, we propose a Handcrafted Normalized-Convolution Network (NmzNet) for efficient texture classification. NmzNet is implemented by a three-layer normalized convolution network, which computes successive normalized convolution with a Predefined Filter bank (Gabor Filter bank) and modulus non-linearities. Coefficients from different layers are aggregated by Fisher Vector aggregation to form the final discriminative features. The results of experimental evaluation on three texture datasets UIUC, KTH-TIPS-2a, and KTH-TIPS-2b indicate that our proposed approach achieves the good classification rate compared with other handcrafted methods. The results additionally indicate that only a marginal difference exists between the best classification rate of recent frontiers CNN and that of the proposed method on the experimented datasets.
Marleen De Bruijne - One of the best experts on this subject based on the ideXlab platform.
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Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines
IEEE Transactions on Medical Imaging, 2016Co-Authors: Gijs Van Tulder, Marleen De BruijneAbstract:The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, Predefined Filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann machines may outperform these standard Filter banks because they learn a feature description directly from the training data. Like many other representation learning methods, restricted Boltzmann machines are unsupervised and are trained with a generative learning objective; this allows them to learn representations from unlabeled data, but does not necessarily produce features that are optimal for classification. In this paper we propose the convolutional classification restricted Boltzmann machine, which combines a generative and a discriminative learning objective. This allows it to learn Filters that are good both for describing the training data and for classification. We present experiments with feature learning for lung texture classification and airway detection in CT images. In both applications, a combination of learning objectives outperformed purely discriminative or generative learning, increasing, for instance, the lung tissue classification accuracy by 1 to 8 percentage points. This shows that discriminative learning can help an otherwise unsupervised feature learner to learn Filters that are optimized for classification.
Ngoc-son Vu - One of the best experts on this subject based on the ideXlab platform.
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ICCV Workshops - A Handcrafted Normalized-Convolution Network for Texture Classification
2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017Co-Authors: Ngoc-son Vu, Vu-lam Nguyen, Philippe-henri GosselinAbstract:In this paper, we propose a Handcrafted Normalized-Convolution Network (NmzNet) for efficient texture classification. NmzNet is implemented by a three-layer normalized convolution network, which computes successive normalized convolution with a Predefined Filter bank (Gabor Filter bank) and modulus non-linearities. Coefficients from different layers are aggregated by Fisher Vector aggregation to form the final discriminative features. The results of experimental evaluation on three texture datasets UIUC, KTH-TIPS-2a, and KTH-TIPS-2b indicate that our proposed approach achieves the good classification rate compared with other handcrafted methods. The results additionally indicate that only a marginal difference exists between the best classification rate of recent frontiers CNN and that of the proposed method on the experimented datasets.
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A Handcrafted Normalized-Convolution Network for Texture Classification
2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017Co-Authors: Ngoc-son Vu, Vu-lam Nguyen, Philippe-henri GosselinAbstract:In this paper, we propose a Handcrafted Normalized-Convolution Network (NmzNet) for efficient texture classification. NmzNet is implemented by a three-layer normalized convolution network, which computes successive normalized convolution with a Predefined Filter bank (Gabor Filter bank) and modulus non-linearities. Coefficients from different layers are aggregated by Fisher Vector aggregation to form the final discriminative features. The results of experimental evaluation on three texture datasets UIUC, KTH-TIPS-2a, and KTH-TIPS-2b indicate that our proposed approach achieves the good classification rate compared with other handcrafted methods. The results additionally indicate that only a marginal difference exists between the best classification rate of recent frontiers CNN and that of the proposed method on the experimented datasets.
Gijs Van Tulder - One of the best experts on this subject based on the ideXlab platform.
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Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines
IEEE Transactions on Medical Imaging, 2016Co-Authors: Gijs Van Tulder, Marleen De BruijneAbstract:The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, Predefined Filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann machines may outperform these standard Filter banks because they learn a feature description directly from the training data. Like many other representation learning methods, restricted Boltzmann machines are unsupervised and are trained with a generative learning objective; this allows them to learn representations from unlabeled data, but does not necessarily produce features that are optimal for classification. In this paper we propose the convolutional classification restricted Boltzmann machine, which combines a generative and a discriminative learning objective. This allows it to learn Filters that are good both for describing the training data and for classification. We present experiments with feature learning for lung texture classification and airway detection in CT images. In both applications, a combination of learning objectives outperformed purely discriminative or generative learning, increasing, for instance, the lung tissue classification accuracy by 1 to 8 percentage points. This shows that discriminative learning can help an otherwise unsupervised feature learner to learn Filters that are optimized for classification.
Vu-lam Nguyen - One of the best experts on this subject based on the ideXlab platform.
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ICCV Workshops - A Handcrafted Normalized-Convolution Network for Texture Classification
2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017Co-Authors: Ngoc-son Vu, Vu-lam Nguyen, Philippe-henri GosselinAbstract:In this paper, we propose a Handcrafted Normalized-Convolution Network (NmzNet) for efficient texture classification. NmzNet is implemented by a three-layer normalized convolution network, which computes successive normalized convolution with a Predefined Filter bank (Gabor Filter bank) and modulus non-linearities. Coefficients from different layers are aggregated by Fisher Vector aggregation to form the final discriminative features. The results of experimental evaluation on three texture datasets UIUC, KTH-TIPS-2a, and KTH-TIPS-2b indicate that our proposed approach achieves the good classification rate compared with other handcrafted methods. The results additionally indicate that only a marginal difference exists between the best classification rate of recent frontiers CNN and that of the proposed method on the experimented datasets.
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A Handcrafted Normalized-Convolution Network for Texture Classification
2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017Co-Authors: Ngoc-son Vu, Vu-lam Nguyen, Philippe-henri GosselinAbstract:In this paper, we propose a Handcrafted Normalized-Convolution Network (NmzNet) for efficient texture classification. NmzNet is implemented by a three-layer normalized convolution network, which computes successive normalized convolution with a Predefined Filter bank (Gabor Filter bank) and modulus non-linearities. Coefficients from different layers are aggregated by Fisher Vector aggregation to form the final discriminative features. The results of experimental evaluation on three texture datasets UIUC, KTH-TIPS-2a, and KTH-TIPS-2b indicate that our proposed approach achieves the good classification rate compared with other handcrafted methods. The results additionally indicate that only a marginal difference exists between the best classification rate of recent frontiers CNN and that of the proposed method on the experimented datasets.