Abstract
Bird species classification is a challenging task in the field of computer vision because of its fine-grained nature, which in turn can lead to high interclass similarities. An important aspect for many fine-grained categorization problems involves processing of local-level semantics. This highlights the need for accurate part detection/localization. In this work, we propose a two-step approach to address the problem of bird part localization from an input image. In the first step, a Faster RCNN (FRCNN) is learnt to suggest possible bird part regions. However, the part region proposals given by Faster RCNN are not always precise. To refine these, a second step involving a CNN-based part classifier, trained only on bird part segments is used. Both FRCNN and CNN part classifiers are trained separately in a supervised manner. The part classifier effectively builds upon the FRCNN region proposals, as it is trained on more specific data as compared to FRCNN. We evaluate the proposed framework on the standard CUB-200-2011 bird dataset, as well as on a newly collected IIT Mandi bird dataset, where the latter is used only during testing.
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Pankajakshan, A., Bhavsar, A. (2020). Faster RCNN-CNN-Based Joint Model for Bird Part Localization in Images. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-32-9291-8_17
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DOI: https://doi.org/10.1007/978-981-32-9291-8_17
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