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Evolutionary multi-objective visual cortex for object classification in natural images
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DOI：10.1016/j.jocs.2015.10.011.png)
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Abstract
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In recent years computer vision systems have used the human visual system as inspiration for solving different tasks such as object detection and classification. Computational models as the artificial visual cortex (AVC) have shown promising results in solving such problems. Thus, this paper proposes a new methodology for creating an image descriptor vector for classification, and at the same time, finding the objects' location within the image. Also, this work implements the brain programming paradigm from a multi-objective perspective in order to improve the performance in the object classification task. This methodology is implemented for training the proposed model in order to classify the images from the GRAZ-01 and GRAZ-02 databases. The solutions found in this research match, and in some cases outperform, other techniques of the state-of-the-art for classifying the aforementioned databases. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Artificial visual cortex
Object classification
Object detection
Multi-objective brain programming
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