Semantic Image Retrieval in a Grid Computing Environment Using Support Vector Machines

2013 ◽  
Vol 57 (2) ◽  
pp. 205-216 ◽  
Author(s):  
A. Irtaza ◽  
M. A. Jaffar ◽  
M. T. Mahmood
Keyword(s):  
Support Vector Machines ◽  
Image Retrieval ◽  
Grid Computing ◽  
Support Vector ◽  
Computing Environment ◽  
Semantic Image Retrieval ◽  
Vector Machines

Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble

2012 ◽  
Vol 39 (3) ◽  
pp. 2385-2396 ◽  
Author(s):  
Ela Yildizer ◽  
Ali Metin Balci ◽  
Mohammad Hassan ◽  
Reda Alhajj
Keyword(s):  
Support Vector Machines ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Vector Machines ◽  
Multiple Support ◽  
Multiple Support Vector Machines

scholarly journals Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval

2006 ◽  
Vol 28 (7) ◽  
pp. 1088-1099 ◽  
Author(s):  
Dacheng Tao ◽  
Xiaoou Tang ◽  
Xuelong Li ◽  
Xindong Wu
Keyword(s):  
Support Vector Machines ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Support Vector ◽  
Random Subspace ◽  
Vector Machines

Shape-based image retrieval using support vector machines, Fourier descriptors and self-organizing maps

2007 ◽  
Vol 177 (8) ◽  
pp. 1878-1891 ◽  
Author(s):  
Wai-Tak Wong ◽  
Frank Y. Shih ◽  
Jung Liu
Keyword(s):  
Support Vector Machines ◽  
Image Retrieval ◽  
Support Vector ◽  
Fourier Descriptors ◽  
Self Organizing Maps ◽  
Vector Machines ◽  
Self Organizing

Large-scale image retrieval using transductive support vector machines

2018 ◽  
Vol 173 ◽  
pp. 2-12 ◽  
Author(s):  
Hakan Cevikalp ◽  
Merve Elmas ◽  
Savas Ozkan
Keyword(s):  
Support Vector Machines ◽  
Image Retrieval ◽  
Large Scale ◽  
Support Vector ◽  
Vector Machines ◽  
Large Scale Image Retrieval

scholarly journals Enhancing Performance of Image Retrieval Systems Using Dual Tree Complex Wavelet Transform and Support Vector Machines

2008 ◽  
Vol 16 (1) ◽  
pp. 57 ◽  
Author(s):  
Adeel Mumtaz ◽  
Syed Asif Mehmood Gilani ◽  
Kamran Hameed ◽  
Tahir Jameel
Keyword(s):  
Wavelet Transform ◽  
Support Vector Machines ◽  
Image Retrieval ◽  
Support Vector ◽  
Complex Wavelet Transform ◽  
Retrieval Systems ◽  
Vector Machines ◽  
Complex Wavelet

scholarly journals IR-MF-SVMe: Image Retrieval using Multiple Features Extraction with Support Vector Machine ensemble

2021 ◽  
Vol 15 ◽  
pp. 1781-1789
Author(s):  
Nayak K., Venkataravana ◽  
J. S. Arunalatha ◽  
K. R. Venugopal
Keyword(s):  
Support Vector Machines ◽  
Image Retrieval ◽  
Feature Representation ◽  
Gabor Wavelet ◽  
Support Vector ◽  
Shape Information ◽  
Discriminative Performance ◽  
Multiple Features ◽  
Vector Machines ◽  
The Individual

Image representation is a widespread strategy of image retrieval based on appearance, shape information. The traditional feature representation methods ignore hidden information that exists in the dataset samples; it reduces the discriminative performance of the classifier and excludes various geometric and photometric variations consideration in obtaining the features; these degrade retrieval performance. Hence, proposed multiple features fusion and Support Vector Machines Ensemble (IR-MF-SVMe); an Image Retrieval framework to enhance the performance of the retrieval process. The Color Histogram (CH), Color Auto-Correlogram (CAC), Color Moments (CM), Gabor Wavelet (GW), and Wavelet Moments (WM) descriptors are used to extract multiple features that separate the element vectors of images in representation. The multi-class classifier is constructed with the aggregation of binary Support Vector Machines, which decrease the count of false positives within the interrelated semantic classes. The proposed framework is validated on the WANG dataset and results in the accuracy of 84% for the individual features and 86% for the fused features related to the state-of-the-arts.

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