Nonparametric Part Transfer for Fine-grained Recognition Presenter Byungju Kim

Fine-grained Recognition 2 Birds category-level classification

Fine-grained Recognition 3 Pelagic CormorantRed faced Cormorant

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) 4

Deformable Part Model 5

Approach 6

Model – Nearest neighbor part transfer HOG Histogram of Oriented Gradients Ratio of the bounding boxes Normalization Flipped photo 7

Model – Nearest neighbor part transfer 8 Training set Test input Cropped imageHOG

Model – Part & Global feature representation Part feature Color descriptors 9

Model – Part & Global feature representation Global feature Bag of visual words with OpponentSIFT and color names Spatial pyramid pooling Using GrabCut segmentation 10

Result CUB-2010, CUB-2011 (200 bird species, bounding box) 11

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 12

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) 13

Thank you! 14