Abstract
Scene classification remains a challenging task in computer vision applications due to a wide range of intraclass and interclass variations. A robust feature extraction technique and an effective classifier are required to achieve satisfactory recognition performance. Herein, we propose a nonregularized state preserving extreme learning machine (NSPELM) to perform scene classification tasks. We employ a Bag-of-Words (BoW) model for feature extraction prior to performing the classification task. The BoW feature is obtained based on a regular grid method for point selection and Speeded Up Robust Features (SURF) technique for feature extraction on the selected points. The performance of NSPELM is tested and evaluated on three standard scene category classification datasets. The recognition accuracy is compared with the standard extreme learning machine classifier and it shows that the proposed NSPELM algorithm yields better accuracy.
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Sidike, P., Alom, M.Z., Asari, V.K., Taha, T.M. (2017). Non-regularized State Preserving Extreme Learning Machine for Natural Scene Classification. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-2104-6_37
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DOI: https://doi.org/10.1007/978-981-10-2104-6_37
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