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

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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