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An Improved Approach for Multi-Task Feature Image Classification using Hybrid GA-SIFT
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 158 - Number 4 |
| Year of Publication: 2017 |
| Authors: Vandna Prajapati, Anil Suryavanshi |
10.5120/ijca2017912777
|
Vandna Prajapati, Anil Suryavanshi . An Improved Approach for Multi-Task Feature Image Classification using Hybrid GA-SIFT. International Journal of Computer Applications. 158, 4 ( Jan 2017), 13-19. DOI=10.5120/ijca2017912777
Abstract
Here in this paper an efficient technique for the Image Classification is proposed using Optimization of SIFT Algorithm by Genetic Algorithm. The Proposed Procedure implemented here is used for the Classification of Single Task as well as Multiple Task Features from the Image and classification is done. The Experimental results achieved on numerous datasets such as MIR Flickr, NUS Datasets shows the recital of the planned methodology. The algorithm provides High Precision and recall rate as well as more number of features extracted from the image with High Accuracy.
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