Abstract
In this paper, we address a problem of detecting regions (bounding box) containing birds in images, which is closely related to the task of fine-grained visual classification (FGVC) of bird images. We note that there exist various sophisticated approaches proposed for this task within the overall framework of FGVC. However, we demonstrate that the problem of bird region detection, by itself, can be addressed in a rather simplistic manner. Our approach employs HOG features and a multi-scale detection framework using the SVM classifier, but where real-valued scores (or weights) from the SVM are used rather than the conventional binary decision labels. We validate our approach on a variety of bird images from the CUB-200 bird image data set and show that the proposed approach yields reasonable quality bird region detection.
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Kumar, R., Kumar, A., Bhavsar, A. (2018). Bird Region Detection in Images with Multi-scale HOG Features and SVM Scoring. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 704. Springer, Singapore. https://doi.org/10.1007/978-981-10-7898-9_29
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DOI: https://doi.org/10.1007/978-981-10-7898-9_29
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