The Experts below are selected from a list of 159879 Experts worldwide ranked by ideXlab platform
Biplab Banerjee - One of the best experts on this subject based on the ideXlab platform.
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a novel semi supervised land cover Classification Technique of remotely sensed images
Journal of The Indian Society of Remote Sensing, 2015Co-Authors: Biplab Banerjee, Krishna Mohan BuddhirajuAbstract:This research article addresses the problem of land-cover Classification from the multi-spectral remotely sensed images using a novel self-training based semi-supervised learning (SSL) Technique. The proposed system, instead of using a single classifier, builds an ensemble of classifiers with the hope that the ensemble system will have a lesser generalization error than any of its members. Each component classifier is trained independently using the proposed self-training approach on different training sub-sets and their predictions on test samples are combined using a new instant run-off voting (IRV) based classifier combination method. Each training subset consists of a few labeled samples and a comparatively large number of unlabeled data items. The cluster-and-label method for self-training has been adopted here considering the support vector machines (SVM) as the supervised learner and minimum spanning tree based clustering. A normalized histogram intersection kernel has been proposed which has shown to outperform the state-of-the-art kernel functions in terms of the SVM generalization accuracy. A novel cluster validity index specifically for graph based clustering has also been introduced to access the quality of the clustering. The proposed training method has the advantage that it can find classes for which the labeled training data are unavailable initially. A number of multi-spectral images have been considered for the experimental evaluation and a comparison with some of the well-established Techniques from the literature has confirmed the superiority of the proposed classifier system.
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a new self training based unsupervised satellite image Classification Technique using cluster ensemble strategy
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Biplab Banerjee, Lorenzo Bruzzone, Francesca Bovolo, Avik Bhattacharya, Subhasis Chaudhuri, Krishna B MohanAbstract:This letter addresses the problem of unsupervised land-cover Classification of remotely sensed multispectral satellite images from the perspective of cluster ensembles and self-learning. The cluster ensembles combine multiple data partitions generated by different clustering algorithms into a single robust solution. A cluster-ensemble-based method is proposed here for the initialization of the unsupervised iterative expectation-maximization (EM) algorithm which eventually produces a better approximation of the cluster parameters considering a certain statistical model is followed to fit the data. The method assumes that the number of land-cover classes is known. A novel method for generating a consistent labeling scheme for each clustering of the consensus is introduced for cluster ensembles. A maximum likelihood classifier is henceforth trained on the updated parameter set obtained from the EM step and is further used to classify the rest of the image pixels. The self-learning classifier, although trained without any external supervision, reduces the effect of data overlapping from different clusters which otherwise a single clustering algorithm fails to identify. The clustering performance of the proposed method on a medium resolution and a very high spatial resolution image have effectively outperformed the results of the individual clustering of the ensemble.
Liang Zhao - One of the best experts on this subject based on the ideXlab platform.
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a semi supervised Classification Technique based on interacting forces
Neurocomputing, 2014Co-Authors: Thiago Henrique Cupertino, Roberto Alves Gueleri, Liang ZhaoAbstract:Semi-supervised learning is a Classification paradigm in which just a few labeled instances are available for the training process. To overcome this small amount of initial label information, the information provided by the unlabeled instances is also considered. In this paper, we propose a nature-inspired semi-supervised learning Technique based on attraction forces. Instances are represented as points in a k-dimensional space, and the movement of data points is modeled as a dynamical system. As the system runs, data items with the same label cooperate with each other, and data items with different labels compete among them to attract unlabeled points by applying a specific force function. In this way, all unlabeled data items can be classified when the system reaches its stable state. Stability analysis for the proposed dynamical system is performed and some heuristics are proposed for parameter setting. Simulation results show that the proposed Technique achieves good Classification results on artificial data sets and is comparable to well-known semi-supervised Techniques using benchmark data sets.
Karen Das - One of the best experts on this subject based on the ideXlab platform.
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indian sign language recognition using eigen value weighted euclidean distance based Classification Technique
arXiv: Computer Vision and Pattern Recognition, 2013Co-Authors: Joyeeta Singha, Karen DasAbstract:Sign Language Recognition is one of the most growing fields of research today. Many new Techniques have been developed recently in these fields. Here in this paper, we have proposed a system using Eigen value weighted Euclidean distance as a Classification Technique for recognition of various Sign Languages of India. The system comprises of four parts: Skin Filtering, Hand Cropping, Feature Extraction and Classification. Twenty four signs were considered in this paper, each having ten samples, thus a total of two hundred forty images was considered for which recognition rate obtained was 97 percent.
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indian sign language recognition using eigen value weighted euclidean distance based Classification Technique
International Journal of Advanced Computer Science and Applications, 2013Co-Authors: Joyeeta Singha, Karen DasAbstract:Sign Language Recognition is one of the most growing fields of research today. Many new Techniques have been developed recently in these fields. Here in this paper, we have proposed a system using Eigen value weighted Euclidean distance as a Classification Technique for recognition of various Sign Languages of India. The system comprises of four parts: Skin Filtering, Hand Cropping, Feature Extraction and Classification. 24 signs were considered in this paper, each having 10 samples, thus a total of 240 images was considered for which recognition rate obtained was 97%.
Jurgen Stausberg - One of the best experts on this subject based on the ideXlab platform.
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evaluation of a binary semi supervised Classification Technique for probabilistic record linkage
Methods of Information in Medicine, 2015Co-Authors: Daniel Nasseh, Jurgen StausbergAbstract:Background: The process of merging data of different data sources is referred to as record linkage. A medical environment with increased preconditions on privacy protection demands the transformation of clear-text attributes like first name or date of birth into one-way encrypted pseudonyms. When performing an automated or privacy preserving record linkage there might be the need of a binary Classification deciding whether two records should be classified as the same entity. The Classification is the final of the four main phases of the record linkage process: Preprocessing, indexing, matching and Classification. The choice of binary Classification Techniques in dependence of project specifications in particular data quality has not extensively been studied yet. Objectives: The aim of this work is the introduction and evaluation of an automatable semi-supervised binary Classification system applied within the field of record linkage capable of competing or even surpassing advanced automated Techniques of the domain of unsupervised Classification. Methods: This work describes the rationale leading to the model and the final implementation of an automatable semi-supervised binary Classification system and the comparison of its Classification performance to an advanced active learning approach out of the domain of unsupervised learning. The performance of both systems has been measured on a broad variety of artificial test sets (n = 400), based on real patient data, with distinct and unique characteristics. Results: While the Classification performance for both methods measured as F- measure was relatively close on test sets with maximum defined data quality, 0.996 for semi-supervised Classification, 0.993 for unsupervised Classification, it incrementally diverged for test sets of worse data quality dropping to 0.964 for semi-supervised Classification and 0.803 for unsupervised Classification. Conclusions: Aside from supplying a viable model for semi-supervised Classification for automated probabilistic record linkage, the tests conducted on a large amount of test sets suggest that semi-supervised Techniques might generally be capable of outperforming unsupervised Techniques especially on data with lower levels of data quality.
Krishna B Mohan - One of the best experts on this subject based on the ideXlab platform.
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a new self training based unsupervised satellite image Classification Technique using cluster ensemble strategy
IEEE Geoscience and Remote Sensing Letters, 2015Co-Authors: Biplab Banerjee, Lorenzo Bruzzone, Francesca Bovolo, Avik Bhattacharya, Subhasis Chaudhuri, Krishna B MohanAbstract:This letter addresses the problem of unsupervised land-cover Classification of remotely sensed multispectral satellite images from the perspective of cluster ensembles and self-learning. The cluster ensembles combine multiple data partitions generated by different clustering algorithms into a single robust solution. A cluster-ensemble-based method is proposed here for the initialization of the unsupervised iterative expectation-maximization (EM) algorithm which eventually produces a better approximation of the cluster parameters considering a certain statistical model is followed to fit the data. The method assumes that the number of land-cover classes is known. A novel method for generating a consistent labeling scheme for each clustering of the consensus is introduced for cluster ensembles. A maximum likelihood classifier is henceforth trained on the updated parameter set obtained from the EM step and is further used to classify the rest of the image pixels. The self-learning classifier, although trained without any external supervision, reduces the effect of data overlapping from different clusters which otherwise a single clustering algorithm fails to identify. The clustering performance of the proposed method on a medium resolution and a very high spatial resolution image have effectively outperformed the results of the individual clustering of the ensemble.