Abstract
Image categorization is the process of classifying all pixels of an image into one of several classes. In this paper, we have proposed a novel vision-based method for image categorization is invariant to affine transformation and robust to cluttered background. The proposed methodology consists of three phases: segmentation, feature extraction, and classification. In segmentation, an object of interest is segmented from the image. Features representing the image are extracted in feature extraction phase. Finally, an image is classified using multi-class support vector machine. The main advantage of this method is that it is simple and computationally efficient. We have tested the performance of proposed system on Caltech 101 object category and reported 76.14 % recognition accuracy.
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Agrawal, S.C., Jalal, A.S., Tripathi, R.K. (2017). A Hybrid Method for Image Categorization Using Shape Descriptors and Histogram of Oriented Gradients. 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_26
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DOI: https://doi.org/10.1007/978-981-10-2104-6_26
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