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International Journal of Innovation and Scientific Research
ISSN: 2351-8014
 
 
Sunday 28 May 2017

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EFFICIENT METHOD FOR IMAGE CLASSIFICATION USING ACO


Volume 30, Issue 3, May 2017, Pages 420–427

 EFFICIENT METHOD FOR IMAGE CLASSIFICATION USING ACO

D. Vinod Kumar and P. Selvakumar

Original language: English

Received 1 January 2017

Copyright © 2017 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract


Classification of objects in an image finds its application in many real-time systems such as video surveillance systems etc. The basic operation is to detect the object present in the image frame. The processes involved are i) Extracting the object features and ii) Feeding the features into a classifier. External factors such as illumination, brightness etc., have profound effect on the process of classification. These conditions can lead to misdetection of objects. Similarly, the selection of features for classification affects the classification efficiency. Hence optimized feature detection, efficient feature extraction and a supportive classifier selection is mandatory for accurate classification. An absolute combination of suitable optimization solution, feature for classification and a matching classifier is presented in this work.

Author Keywords: ACO, Pheromone, heuristics, Bayesian classification.


How to Cite this Article


D. Vinod Kumar and P. Selvakumar, “EFFICIENT METHOD FOR IMAGE CLASSIFICATION USING ACO,” International Journal of Innovation and Scientific Research, vol. 30, no. 3, pp. 420–427, May 2017.