Abstract
Visual object recognition is an extremely difficult computational problem. It is still a challenging task for computer vision systems related especially to the high variability of the image of objects that may vary somewhat in different viewpoints, in many different sizes and scales or even when they are translated or rotated. In this study, we investigate the combination of a new dynamic random forests and SURF descriptor for object recognition. We have carried out experiments on two benchmark object recognition datasets: CIFAR-10 and STL-10. The experimental results show the superior ability of our proposed approach, compared to the standard RF in terms of recognition rate and execution time.
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Jayech, K., Mahjoub, M.A. (2017). Object Recognition Based on Dynamic Random Forests and SURF Descriptor. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2017. IDEAL 2017. Lecture Notes in Computer Science(), vol 10585. Springer, Cham. https://doi.org/10.1007/978-3-319-68935-7_39
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DOI: https://doi.org/10.1007/978-3-319-68935-7_39
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