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Appl. Sci. 2018, 8(4), 495; https://doi.org/10.3390/app8040495

An Ensemble Based Evolutionary Approach to the Class Imbalance Problem with Applications in CBIR

1
Department of Computer Science, University of Engineering and Technology, Taxila 47080, Pakistan
2
Department of Computer Science, University of Central Punjab, Lahore 54000, Pakistan
3
College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
4
Department of Computer Science, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan
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Department of Software Engineering, University of Engineering and Technology, Taxila 47080, Pakistan
6
School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolno, Byeogchunmyun, Cheonan 31253, Korea
*
Author to whom correspondence should be addressed.
Received: 12 February 2018 / Revised: 16 March 2018 / Accepted: 19 March 2018 / Published: 26 March 2018
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PDF [1934 KB, uploaded 26 March 2018]
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Abstract

In order to lower the dependence on textual annotations for image searches, the content based image retrieval (CBIR) has become a popular topic in computer vision. A wide range of CBIR applications consider classification techniques, such as artificial neural networks (ANN), support vector machines (SVM), etc. to understand the query image content to retrieve relevant output. However, in multi-class search environments, the retrieval results are far from optimal due to overlapping semantics amongst subjects of various classes. The classification through multiple classifiers generate better results, but as the number of negative examples increases due to highly correlated semantic classes, classification bias occurs towards the negative class, hence, the combination of the classifiers become even more unstable particularly in one-against-all classification scenarios. In order to resolve this issue, a genetic algorithm (GA) based classifier comity learning (GCCL) method is presented in this paper to generate stable classifiers by combining ANN with SVMs through asymmetric and symmetric bagging. The proposed approach resolves the classification disagreement amongst different classifiers and also resolves the class imbalance problem in CBIR. Once the stable classifiers are generated, the query image is presented to the trained model to understand the underlying semantic content of the query image for association with the precise semantic class. Afterwards, the feature similarity is computed within the obtained class to generate the semantic response of the system. The experiments reveal that the proposed method outperforms various state-of-the-art methods and significantly improves the image retrieval performance. View Full-Text
Keywords: CBIR; genetic algorithms; SVM; neural networks; semantic association; asymmetric bagging CBIR; genetic algorithms; SVM; neural networks; semantic association; asymmetric bagging
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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 (CC BY 4.0).
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Irtaza, A.; Adnan, S.M.; Ahmed, K.T.; Jaffar, A.; Khan, A.; Javed, A.; Mahmood, M.T. An Ensemble Based Evolutionary Approach to the Class Imbalance Problem with Applications in CBIR. Appl. Sci. 2018, 8, 495.

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