Image Feature Selection using Ant Colony Optimization
Richa Sharma1, Anuradha Purohit2
1Richa Sharma*, Department of Computer Engineering, Shri G.S. Institute of Technology and Science, Indore, Madhya Pradesh, India.
2Anuradha Purohit, Department of Computer Engineering, Shri G.S. Institute of Technology and Science, Indore, Madhya Pradesh, India.
Manuscript received on October 16, 2019. | Revised Manuscript received on 26 October, 2019. | Manuscript published on November 10, 2019. | PP: 2669-2674 | Volume-9 Issue-1, November 2019. | Retrieval Number: L33961081219/2019©BEIESP | DOI: 10.35940/ijitee.L3396.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the enhancement in imaging technology, huge amount of information is being created whose classification is a challenging task among researchers. The classification performance is solely dependent on the quality of the features defining the respective images. Hence, feature extraction from images and feature selection from this huge data is required. Image Feature Selection is a vital step which can significantly affect the performance of image classification system. This paper proposes an efficient combination of image feature extraction technique and feature selection strategy using Ant Colony Optimization (ACO). ACO utilizes overall subsets performance and local feature importance to fetch problem domain finding prime solutions. For ACO based feature selection in images most of the researchers use priori information of features. However, in this work, the features are extracted separately through the detailed analytical process which carries the best suited features for different classes of images. The performance in terms of classification accuracy has been enhanced through the optimization of features in the dataset. For experimentation and evaluating the performance of proposed work, Corel and Caltech datasets are used. Satisfactory results have been obtained with the proposed approach.
Keywords: Image Feature Selection, Feature Extraction, Ant Colony Optimization, MATLAB, Grey Level Co-occurrence Matrix.
Scope of the Article: Discrete Optimization