nonparametric part transfer for fine-grained recognition presenter byungju kim
TRANSCRIPT
![Page 1: Nonparametric Part Transfer for Fine-grained Recognition Presenter Byungju Kim](https://reader031.vdocuments.mx/reader031/viewer/2022020219/56649e8e5503460f94b91bb9/html5/thumbnails/1.jpg)
Nonparametric Part Transfer for Fine-grained Recognition
Presenter Byungju Kim
![Page 2: Nonparametric Part Transfer for Fine-grained Recognition Presenter Byungju Kim](https://reader031.vdocuments.mx/reader031/viewer/2022020219/56649e8e5503460f94b91bb9/html5/thumbnails/2.jpg)
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Fine-grained Recognition
Birdscategory-level classification
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Fine-grained Recognition
Pelagic CormorantRed faced Cormorant
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Assumption
• Test Data• Category-level classification is done• Bounding box at the object
• Training Data• Bounding boxes at each part
• Dataset : CUB-2011 (6033 birds image, 200 species)
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Deformable Part Model
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Approach
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Model – Nearest neighbor part trans-fer• HOG
• Histogram of Oriented Gradients
• Ratio of the bounding boxes• Normalization• Flipped photo
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Model – Nearest neighbor part trans-fer
Training setTest input
Cropped image HOG
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Model – Part & Global feature representa-tion• Part feature• Color descriptors
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Model – Part & Global feature representa-tion• Global feature• Bag of visual words with OpponentSIFT and color names• Spatial pyramid pooling• Using GrabCut segmentation
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Result
• CUB-2010, CUB-2011 (200 bird species, bounding box)
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Conclusion
• Good performance with simple feature• Imply the importance of the part location
• Complex background can effect the result
• Modifying the part region could make the performance better
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Quiz
1. Unlike DPM, they didn’t used HOG to classify the species of the birds.(T/F)
2. In this paper, they focused on finding the position of each parts.(T/F)
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Thank you!