The Experts below are selected from a list of 296211 Experts worldwide ranked by ideXlab platform
Lorenzo Bruzzone - One of the best experts on this subject based on the ideXlab platform.
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A Spectral-Spatial Multicriteria Active Learning Technique for Hyperspectral Image Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Swarnajyoti Patra, Kaushal Bhardwaj, Lorenzo BruzzoneAbstract:Hyperspectral image classification with limited labeled samples is a challenging task and still an open research issue. In this article, a novel Technique is presented to address such an issue by exploiting dimensionality reduction, spectral-spatial information, and classification with active Learning. The proposed Technique is based on two phases. Considering the importance of dimensionality reduction and spatial information for the analysis of hyperspectral images, Phase I generates the patterns corresponding to each pixel of the image using both spectral and spatial information. To this end, first, principal component analysis is used to reduce the dimensionality of an hyperspectral image, then extended morphological profiles are exploited. The spectral-spatial information based patterns generated by extended morphological profiles are used as input to the Phase II. Phase II performs the classification task guided by an active Learning Technique. This Technique is based on a novel query function that uses uncertainty, diversity, and cluster assumption criteria by exploiting the properties of $k$ -means clustering, $K$ -nearest neighbors algorithm, support vector machines, and genetic algorithms. Experiments on three benchmark hyperspectral datasets demonstrate that the proposed method outperforms five state-of-the-art active Learning methods.
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A Novel SOM-SVM-Based Active Learning Technique for Remote Sensing Image Classification
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Swarnajyoti Patra, Lorenzo BruzzoneAbstract:In this paper, a novel iterative active Learning Technique based on self-organizing map (SOM) neural network and support vector machine (SVM) classifier is presented. The Technique exploits the properties of the SVM classifier and of the SOM neural network to identify uncertain and diverse samples, to include in the training set. It selects uncertain samples from low-density regions of the feature space by exploiting the topological properties of the SOM. This results in a fast convergence also when the available initial training samples are poor. The effectiveness of the proposed method is assessed by comparing it with several methods existing in the literature using a toy data set and a color image as well as real multispectral and hyperspectral remote sensing images.
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a novel som based active Learning Technique for classification of remote sensing images with svm
International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Swarnajyoti Patra, Lorenzo BruzzoneAbstract:This paper presents a novel batch mode active Learning Technique for solving remote sensing image classification problems. The proposed Technique incorporates uncertainty, diversity and cluster assumption criteria to design the query function. The uncertainty criterion is implemented by taking into account the properties of the support vector machine classifiers. The diversity and cluster assumption criteria are defined by exploiting the properties of the self-organizing map neural networks. To assess the effectiveness of the proposed method, we compared it with several other active Learning methods existing in the remote sensing literature by using both multispectral and hyperspectral remote sensing data sets. Experimental results confirmed the effectiveness of the proposed Technique.
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IGARSS - A novel SOM-based active Learning Technique for classification of remote sensing images with SVM
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Swarnajyoti Patra, Lorenzo BruzzoneAbstract:This paper presents a novel batch mode active Learning Technique for solving remote sensing image classification problems. The proposed Technique incorporates uncertainty, diversity and cluster assumption criteria to design the query function. The uncertainty criterion is implemented by taking into account the properties of the support vector machine classifiers. The diversity and cluster assumption criteria are defined by exploiting the properties of the self-organizing map neural networks. To assess the effectiveness of the proposed method, we compared it with several other active Learning methods existing in the remote sensing literature by using both multispectral and hyperspectral remote sensing data sets. Experimental results confirmed the effectiveness of the proposed Technique.
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a cost sensitive active Learning Technique for the definition of effective training sets for supervised classifiers
International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Begum Demir, Luca Minello, Lorenzo BruzzoneAbstract:This paper presents a novel cost-sensitive active Learning Technique (CSAL) to define effective training sets for the classification of remote sensing images. Unlike the standard active Learning methods, the proposed Technique redefines AL by assuming that the labeling cost of samples when ground survey is used is not uniform and depends both on the samples accessibility and the traveling time to the considered locations. Accordingly, the proposed CSAL Technique is based on the joint evaluation of three criteria for the selection of the most informative samples that have a low labeling cost: i) uncertainty, ii) diversity and iii) cost efficiency. The labeling cost of the samples is assessed by using ancillary data like the road map and the digital elevation model of the considered area. Experimental results show the effectiveness of the proposed CSAL method compared to the standard active Learning methods that neglect the labeling cost.
Swarnajyoti Patra - One of the best experts on this subject based on the ideXlab platform.
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A Spectral-Spatial Multicriteria Active Learning Technique for Hyperspectral Image Classification
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017Co-Authors: Swarnajyoti Patra, Kaushal Bhardwaj, Lorenzo BruzzoneAbstract:Hyperspectral image classification with limited labeled samples is a challenging task and still an open research issue. In this article, a novel Technique is presented to address such an issue by exploiting dimensionality reduction, spectral-spatial information, and classification with active Learning. The proposed Technique is based on two phases. Considering the importance of dimensionality reduction and spatial information for the analysis of hyperspectral images, Phase I generates the patterns corresponding to each pixel of the image using both spectral and spatial information. To this end, first, principal component analysis is used to reduce the dimensionality of an hyperspectral image, then extended morphological profiles are exploited. The spectral-spatial information based patterns generated by extended morphological profiles are used as input to the Phase II. Phase II performs the classification task guided by an active Learning Technique. This Technique is based on a novel query function that uses uncertainty, diversity, and cluster assumption criteria by exploiting the properties of $k$ -means clustering, $K$ -nearest neighbors algorithm, support vector machines, and genetic algorithms. Experiments on three benchmark hyperspectral datasets demonstrate that the proposed method outperforms five state-of-the-art active Learning methods.
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A fast partition-based batch-mode active Learning Technique using SVM classifier
Soft Computing, 2017Co-Authors: Anshu Singla, Swarnajyoti PatraAbstract:The selection of informative samples known as query selection is the most challenging task in active Learning. In this article, a batch-mode active Learning Technique is presented by defining a novel query function. The proposed Technique first divides the unlabeled samples into uniform partitions in one-dimensional feature space according to their distribution in the original feature space. Then to select the most informative samples from the unlabeled pool, one sample from each partition is selected based on an uncertainty criterion defined by exploiting SVM classifier. The number of unlabeled samples selected at each iteration of active Learning is determined automatically and depends on the number of non-empty partitions generated. The effectiveness of the proposed Technique is measured by comparing it with four state-of-the-art Techniques exist in the literature by using four different UCI repository data sets. The experimental analysis proved that the proposed Technique is robust and computationally less demanding.
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A Novel SOM-SVM-Based Active Learning Technique for Remote Sensing Image Classification
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Swarnajyoti Patra, Lorenzo BruzzoneAbstract:In this paper, a novel iterative active Learning Technique based on self-organizing map (SOM) neural network and support vector machine (SVM) classifier is presented. The Technique exploits the properties of the SVM classifier and of the SOM neural network to identify uncertain and diverse samples, to include in the training set. It selects uncertain samples from low-density regions of the feature space by exploiting the topological properties of the SOM. This results in a fast convergence also when the available initial training samples are poor. The effectiveness of the proposed method is assessed by comparing it with several methods existing in the literature using a toy data set and a color image as well as real multispectral and hyperspectral remote sensing images.
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a novel som based active Learning Technique for classification of remote sensing images with svm
International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Swarnajyoti Patra, Lorenzo BruzzoneAbstract:This paper presents a novel batch mode active Learning Technique for solving remote sensing image classification problems. The proposed Technique incorporates uncertainty, diversity and cluster assumption criteria to design the query function. The uncertainty criterion is implemented by taking into account the properties of the support vector machine classifiers. The diversity and cluster assumption criteria are defined by exploiting the properties of the self-organizing map neural networks. To assess the effectiveness of the proposed method, we compared it with several other active Learning methods existing in the remote sensing literature by using both multispectral and hyperspectral remote sensing data sets. Experimental results confirmed the effectiveness of the proposed Technique.
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IGARSS - A novel SOM-based active Learning Technique for classification of remote sensing images with SVM
2012 IEEE International Geoscience and Remote Sensing Symposium, 2012Co-Authors: Swarnajyoti Patra, Lorenzo BruzzoneAbstract:This paper presents a novel batch mode active Learning Technique for solving remote sensing image classification problems. The proposed Technique incorporates uncertainty, diversity and cluster assumption criteria to design the query function. The uncertainty criterion is implemented by taking into account the properties of the support vector machine classifiers. The diversity and cluster assumption criteria are defined by exploiting the properties of the self-organizing map neural networks. To assess the effectiveness of the proposed method, we compared it with several other active Learning methods existing in the remote sensing literature by using both multispectral and hyperspectral remote sensing data sets. Experimental results confirmed the effectiveness of the proposed Technique.
Hongying Zhang - One of the best experts on this subject based on the ideXlab platform.
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single image super resolution algorithm with a new dictionary Learning Technique k eigen decomposition
International Conference on Image and Graphics, 2015Co-Authors: Yingyue Zhou, Hongbin Zang, Hongying ZhangAbstract:In this paper, we propose an algorithm to improve some important details of sparse representation based image super resolution (SR) framework. Firstly, a new dictionary Learning Technique K-Eigen decomposition (K-EIG) is proposed. It improves the classical K-SVD algorithm in dictionary atom updating. K-EIG accelerates the Learning process and keeps the similar performance of the learned dictionary. Secondly, image patch classification and edge patches extension are integrated into the SR framework. Two over-complete dictionary-pairs are trained based on K-EIG. In reconstruction, the input low resolution (LR) image is split into patches and each one is classified. The patch type decides which dictionary-pair is chosen. Then the sparse representation coefficient of the LR signal is inferred and the corresponding high resolution (HR) patch can be reconstructed. Experimental results prove that our algorithm can obtain competitive SR performance when compared with some classical methods. Besides, the time-consuming of dictionary-pair Learning is lower.
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ICIG (3) - Single Image Super Resolution Algorithm with a New Dictionary Learning Technique K-Eigen Decomposition
Lecture Notes in Computer Science, 2015Co-Authors: Yingyue Zhou, Hongbin Zang, Hongying ZhangAbstract:In this paper, we propose an algorithm to improve some important details of sparse representation based image super resolution (SR) framework. Firstly, a new dictionary Learning Technique K-Eigen decomposition (K-EIG) is proposed. It improves the classical K-SVD algorithm in dictionary atom updating. K-EIG accelerates the Learning process and keeps the similar performance of the learned dictionary. Secondly, image patch classification and edge patches extension are integrated into the SR framework. Two over-complete dictionary-pairs are trained based on K-EIG. In reconstruction, the input low resolution (LR) image is split into patches and each one is classified. The patch type decides which dictionary-pair is chosen. Then the sparse representation coefficient of the LR signal is inferred and the corresponding high resolution (HR) patch can be reconstructed. Experimental results prove that our algorithm can obtain competitive SR performance when compared with some classical methods. Besides, the time-consuming of dictionary-pair Learning is lower.
Yingyue Zhou - One of the best experts on this subject based on the ideXlab platform.
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single image super resolution algorithm with a new dictionary Learning Technique k eigen decomposition
International Conference on Image and Graphics, 2015Co-Authors: Yingyue Zhou, Hongbin Zang, Hongying ZhangAbstract:In this paper, we propose an algorithm to improve some important details of sparse representation based image super resolution (SR) framework. Firstly, a new dictionary Learning Technique K-Eigen decomposition (K-EIG) is proposed. It improves the classical K-SVD algorithm in dictionary atom updating. K-EIG accelerates the Learning process and keeps the similar performance of the learned dictionary. Secondly, image patch classification and edge patches extension are integrated into the SR framework. Two over-complete dictionary-pairs are trained based on K-EIG. In reconstruction, the input low resolution (LR) image is split into patches and each one is classified. The patch type decides which dictionary-pair is chosen. Then the sparse representation coefficient of the LR signal is inferred and the corresponding high resolution (HR) patch can be reconstructed. Experimental results prove that our algorithm can obtain competitive SR performance when compared with some classical methods. Besides, the time-consuming of dictionary-pair Learning is lower.
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ICIG (3) - Single Image Super Resolution Algorithm with a New Dictionary Learning Technique K-Eigen Decomposition
Lecture Notes in Computer Science, 2015Co-Authors: Yingyue Zhou, Hongbin Zang, Hongying ZhangAbstract:In this paper, we propose an algorithm to improve some important details of sparse representation based image super resolution (SR) framework. Firstly, a new dictionary Learning Technique K-Eigen decomposition (K-EIG) is proposed. It improves the classical K-SVD algorithm in dictionary atom updating. K-EIG accelerates the Learning process and keeps the similar performance of the learned dictionary. Secondly, image patch classification and edge patches extension are integrated into the SR framework. Two over-complete dictionary-pairs are trained based on K-EIG. In reconstruction, the input low resolution (LR) image is split into patches and each one is classified. The patch type decides which dictionary-pair is chosen. Then the sparse representation coefficient of the LR signal is inferred and the corresponding high resolution (HR) patch can be reconstructed. Experimental results prove that our algorithm can obtain competitive SR performance when compared with some classical methods. Besides, the time-consuming of dictionary-pair Learning is lower.
Hongbin Zang - One of the best experts on this subject based on the ideXlab platform.
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single image super resolution algorithm with a new dictionary Learning Technique k eigen decomposition
International Conference on Image and Graphics, 2015Co-Authors: Yingyue Zhou, Hongbin Zang, Hongying ZhangAbstract:In this paper, we propose an algorithm to improve some important details of sparse representation based image super resolution (SR) framework. Firstly, a new dictionary Learning Technique K-Eigen decomposition (K-EIG) is proposed. It improves the classical K-SVD algorithm in dictionary atom updating. K-EIG accelerates the Learning process and keeps the similar performance of the learned dictionary. Secondly, image patch classification and edge patches extension are integrated into the SR framework. Two over-complete dictionary-pairs are trained based on K-EIG. In reconstruction, the input low resolution (LR) image is split into patches and each one is classified. The patch type decides which dictionary-pair is chosen. Then the sparse representation coefficient of the LR signal is inferred and the corresponding high resolution (HR) patch can be reconstructed. Experimental results prove that our algorithm can obtain competitive SR performance when compared with some classical methods. Besides, the time-consuming of dictionary-pair Learning is lower.
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ICIG (3) - Single Image Super Resolution Algorithm with a New Dictionary Learning Technique K-Eigen Decomposition
Lecture Notes in Computer Science, 2015Co-Authors: Yingyue Zhou, Hongbin Zang, Hongying ZhangAbstract:In this paper, we propose an algorithm to improve some important details of sparse representation based image super resolution (SR) framework. Firstly, a new dictionary Learning Technique K-Eigen decomposition (K-EIG) is proposed. It improves the classical K-SVD algorithm in dictionary atom updating. K-EIG accelerates the Learning process and keeps the similar performance of the learned dictionary. Secondly, image patch classification and edge patches extension are integrated into the SR framework. Two over-complete dictionary-pairs are trained based on K-EIG. In reconstruction, the input low resolution (LR) image is split into patches and each one is classified. The patch type decides which dictionary-pair is chosen. Then the sparse representation coefficient of the LR signal is inferred and the corresponding high resolution (HR) patch can be reconstructed. Experimental results prove that our algorithm can obtain competitive SR performance when compared with some classical methods. Besides, the time-consuming of dictionary-pair Learning is lower.
