Nonparametric Part Transfer for Fine-grained Recognition

Presenter Byungju Kim

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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

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!