The Experts below are selected from a list of 33 Experts worldwide ranked by ideXlab platform

Jagruti Ketan Save - One of the best experts on this subject based on the ideXlab platform.

  • Generation of an Effective Training Feature Vector using VQ for Classification of Image Database
    International Journal of Computer Applications, 2014
    Co-Authors: H. B. Kekre, Tanuja Sarode, Jagruti Ketan Save
    Abstract:

    In supervised classification of image database, Feature Vectors of images with known classes, are used for Training purpose. Feature Vectors are extracted in such a way that it will represent maximum information in minimum elements. Accuracy of classification highly depends on the content of Training Feature Vectors and number of Training Feature Vectors. If the number of Training images increases then the performance of classification also improves. But it also leads to more storage space and computation time. The main aim of this research is to reduce the number of Feature Vectors in an effective way so as to reduce memory space required and computation time as well as to increase an accuracy. This paper proposes three major steps for automatic classification of image database. First step is the generation of Feature Vector of an image using column transform, row mean Vector and fusion method. Then Vector Quantization (code book size 4,8 and 16) is applied to reduce the number of Training Feature Vectors per class and generate an effective and compact representation of them. Finally nearest neighbor classification algorithm is used as a classifier. The experiments are conducted on augmented Wang database. The results for various transforms, different similarity measures, varying sizes of Feature Vector, three code book sizes and different number of Training images, are analyzed and compared. Results show that the proposed method increases accuracy in most of the cases. General Terms Image Classification, Vector quantization, Algorithms, Image Database.

  • An Efficient Method for Similarity Measure in Independent PCA based Classification
    INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2013
    Co-Authors: H. B. Kekre, Tanuja Sarode, Jagruti Ketan Save
    Abstract:

    The paper presents a new approach of finding nearest neighbor in image classification algorithm by proposing efficient method for similarity measure. Generally in supervised classification, after finding the Feature Vectors of Training images and testing images, nearest neighbor classifier does the classification job. This classifier uses different distance measures such as Euclidean distance, Manhattan distance etc. to find the nearest Training Feature Vector. This paper proposes to use Mean Squared Error (MSE) to find the nearness between two images. Initially Independent Principal Component Analysis (PCA),which we discussed in our earlier work, is applied to images of each class to generate Eigen coordinate system for that class. Then for the given test image, a set of Feature Vectors is generated. New images are reconstructed using each Eigen coordinate system and the corresponding test Feature Vector. Lowest MSE between the given test image and new reconstructed image indicates the corresponding class for that image. The experiments are conducted on COIL-100 database. The performance is also compared with  distance based nearest neighbor classifier. Results show that the proposed method achieves high accuracy even for small size of Training set.

H. B. Kekre - One of the best experts on this subject based on the ideXlab platform.

  • Generation of an Effective Training Feature Vector using VQ for Classification of Image Database
    International Journal of Computer Applications, 2014
    Co-Authors: H. B. Kekre, Tanuja Sarode, Jagruti Ketan Save
    Abstract:

    In supervised classification of image database, Feature Vectors of images with known classes, are used for Training purpose. Feature Vectors are extracted in such a way that it will represent maximum information in minimum elements. Accuracy of classification highly depends on the content of Training Feature Vectors and number of Training Feature Vectors. If the number of Training images increases then the performance of classification also improves. But it also leads to more storage space and computation time. The main aim of this research is to reduce the number of Feature Vectors in an effective way so as to reduce memory space required and computation time as well as to increase an accuracy. This paper proposes three major steps for automatic classification of image database. First step is the generation of Feature Vector of an image using column transform, row mean Vector and fusion method. Then Vector Quantization (code book size 4,8 and 16) is applied to reduce the number of Training Feature Vectors per class and generate an effective and compact representation of them. Finally nearest neighbor classification algorithm is used as a classifier. The experiments are conducted on augmented Wang database. The results for various transforms, different similarity measures, varying sizes of Feature Vector, three code book sizes and different number of Training images, are analyzed and compared. Results show that the proposed method increases accuracy in most of the cases. General Terms Image Classification, Vector quantization, Algorithms, Image Database.

  • An Efficient Method for Similarity Measure in Independent PCA based Classification
    INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2013
    Co-Authors: H. B. Kekre, Tanuja Sarode, Jagruti Ketan Save
    Abstract:

    The paper presents a new approach of finding nearest neighbor in image classification algorithm by proposing efficient method for similarity measure. Generally in supervised classification, after finding the Feature Vectors of Training images and testing images, nearest neighbor classifier does the classification job. This classifier uses different distance measures such as Euclidean distance, Manhattan distance etc. to find the nearest Training Feature Vector. This paper proposes to use Mean Squared Error (MSE) to find the nearness between two images. Initially Independent Principal Component Analysis (PCA),which we discussed in our earlier work, is applied to images of each class to generate Eigen coordinate system for that class. Then for the given test image, a set of Feature Vectors is generated. New images are reconstructed using each Eigen coordinate system and the corresponding test Feature Vector. Lowest MSE between the given test image and new reconstructed image indicates the corresponding class for that image. The experiments are conducted on COIL-100 database. The performance is also compared with  distance based nearest neighbor classifier. Results show that the proposed method achieves high accuracy even for small size of Training set.

Tanuja Sarode - One of the best experts on this subject based on the ideXlab platform.

  • Generation of an Effective Training Feature Vector using VQ for Classification of Image Database
    International Journal of Computer Applications, 2014
    Co-Authors: H. B. Kekre, Tanuja Sarode, Jagruti Ketan Save
    Abstract:

    In supervised classification of image database, Feature Vectors of images with known classes, are used for Training purpose. Feature Vectors are extracted in such a way that it will represent maximum information in minimum elements. Accuracy of classification highly depends on the content of Training Feature Vectors and number of Training Feature Vectors. If the number of Training images increases then the performance of classification also improves. But it also leads to more storage space and computation time. The main aim of this research is to reduce the number of Feature Vectors in an effective way so as to reduce memory space required and computation time as well as to increase an accuracy. This paper proposes three major steps for automatic classification of image database. First step is the generation of Feature Vector of an image using column transform, row mean Vector and fusion method. Then Vector Quantization (code book size 4,8 and 16) is applied to reduce the number of Training Feature Vectors per class and generate an effective and compact representation of them. Finally nearest neighbor classification algorithm is used as a classifier. The experiments are conducted on augmented Wang database. The results for various transforms, different similarity measures, varying sizes of Feature Vector, three code book sizes and different number of Training images, are analyzed and compared. Results show that the proposed method increases accuracy in most of the cases. General Terms Image Classification, Vector quantization, Algorithms, Image Database.

  • An Efficient Method for Similarity Measure in Independent PCA based Classification
    INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2013
    Co-Authors: H. B. Kekre, Tanuja Sarode, Jagruti Ketan Save
    Abstract:

    The paper presents a new approach of finding nearest neighbor in image classification algorithm by proposing efficient method for similarity measure. Generally in supervised classification, after finding the Feature Vectors of Training images and testing images, nearest neighbor classifier does the classification job. This classifier uses different distance measures such as Euclidean distance, Manhattan distance etc. to find the nearest Training Feature Vector. This paper proposes to use Mean Squared Error (MSE) to find the nearness between two images. Initially Independent Principal Component Analysis (PCA),which we discussed in our earlier work, is applied to images of each class to generate Eigen coordinate system for that class. Then for the given test image, a set of Feature Vectors is generated. New images are reconstructed using each Eigen coordinate system and the corresponding test Feature Vector. Lowest MSE between the given test image and new reconstructed image indicates the corresponding class for that image. The experiments are conducted on COIL-100 database. The performance is also compared with  distance based nearest neighbor classifier. Results show that the proposed method achieves high accuracy even for small size of Training set.

Jason G. Parker - One of the best experts on this subject based on the ideXlab platform.

  • Predicted disease compositions of human gliomas estimated from multiparametric MRI can predict endothelial proliferation, tumor grade, and overall survival.
    arXiv: Quantitative Methods, 2019
    Co-Authors: Emily E. Diller, Sha Cao, Robert M. Lober, Jason G. Parker
    Abstract:

    Background and Purpose: Biopsy is the main determinants of glioma clinical management, but require invasive sampling that fail to detect relevant Features because of tumor heterogeneity. The purpose of this study was to evaluate the accuracy of a voxel-wise, multiparametric MRI radiomic method to predict Features and develop a minimally invasive method to objectively assess neoplasms. Methods: Multiparametric MRI were registered to T1-weighted gadolinium contrast-enhanced data using a 12 degree-of-freedom affine model. The retrospectively collected MRI data included T1-weighted, T1-weighted gadolinium contrast-enhanced, T2-weighted, fluid attenuated inversion recovery, and multi-b-value diffusion-weighted acquired at 1.5T or 3.0T. Clinical experts provided voxel-wise annotations for five disease states on a subset of patients to establish a Training Feature Vector of 611,930 observations. Then, a k-nearest-neighbor (k-NN) classifier was trained using a 25% hold-out design. The trained k-NN model was applied to 13,018,171 observations from seventeen histologically confirmed glioma patients. Linear regression tested overall survival (OS) relationship to predicted disease compositions (PDC) and diagnostic age (alpha = 0.05). Canonical discriminant analysis tested if PDC and diagnostic age could differentiate clinical, genetic, and microscopic factors (alpha = 0.05). Results: The model predicted voxel annotation class with a Dice similarity coefficient of 94.34% +/- 2.98. Linear combinations of PDCs and diagnostic age predicted OS (p = 0.008), grade (p = 0.014), and endothelia proliferation (p = 0.003); but fell short predicting gene mutations for TP53BP1 and IDH1. Conclusions: This voxel-wise, multi-parametric MRI radiomic strategy holds potential as a non-invasive decision-making aid for clinicians managing patients with glioma.

Zlatanov Nikola - One of the best experts on this subject based on the ideXlab platform.

  • On Supervised Classification of Feature Vectors with Independent and Non-Identically Distributed Elements
    2021
    Co-Authors: Shahrivari Farzad, Zlatanov Nikola
    Abstract:

    In this paper, we investigate the problem of classifying Feature Vectors with mutually independent but non-identically distributed elements. First, we show the importance of this problem. Next, we propose a classifier and derive an analytical upper bound on its error probability. We show that the error probability goes to zero as the length of the Feature Vectors grows, even when there is only one Training Feature Vector per label available. Thereby, we show that for this important problem at least one asymptotically optimal classifier exists. Finally, we provide numerical examples where we show that the performance of the proposed classifier outperforms conventional classification algorithms when the number of Training data is small and the length of the Feature Vectors is sufficiently high