Volume 30, Issue 3, May 2017, Pages 420–427
D. Vinod Kumar1 and P. Selvakumar2
1 Dept. of Electronics Engineering, PET Group of Institutions, India
2 Assistant Professor and Head, Department of ECE, PET Group of Institutions, India
Original language: English
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.
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.
D. Vinod Kumar1 and P. Selvakumar2
1 Dept. of Electronics Engineering, PET Group of Institutions, India
2 Assistant Professor and Head, Department of ECE, PET Group of Institutions, India
Original language: English
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.
