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|Title:||Hierarchical part matching for fine-grained visual categorization||Authors:||Xie, L.
|Keywords:||Fine-Grained Visual Categorization
Foreground Inference and Segmentation
Geometric Phrase Pooling
Hierarchical Part Matching
Hierarchical Structure Learning
|Issue Date:||2013||Citation:||Xie, L., Tian, Q., Hong, R., Yan, S., Zhang, B. (2013). Hierarchical part matching for fine-grained visual categorization. Proceedings of the IEEE International Conference on Computer Vision : 1641-1648. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCV.2013.206||Abstract:||As a special topic in computer vision, fine-grained visual categorization (FGVC) has been attracting growing attention these years. Different with traditional image classification tasks in which objects have large inter-class variation, the visual concepts in the fine-grained datasets, such as hundreds of bird species, often have very similar semantics. Due to the large inter-class similarity, it is very difficult to classify the objects without locating really discriminative features, therefore it becomes more important for the algorithm to make full use of the part information in order to train a robust model. In this paper, we propose a powerful flowchart named Hierarchical Part Matching (HPM) to cope with fine-grained classification tasks. We extend the Bag-of-Features (BoF) model by introducing several novel modules to integrate into image representation, including foreground inference and segmentation, Hierarchical Structure Learning (HSL), and Geometric Phrase Pooling (GPP). We verify in experiments that our algorithm achieves the state-of-the-art classification accuracy in the Caltech-UCSD-Birds-200-2011 dataset by making full use of the ground-truth part annotations. © 2013 IEEE.||Source Title:||Proceedings of the IEEE International Conference on Computer Vision||URI:||http://scholarbank.nus.edu.sg/handle/10635/83775||ISBN:||9781479928392||DOI:||10.1109/ICCV.2013.206|
|Appears in Collections:||Staff Publications|
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