of checking all possible windows on each image, we use EdgeBox [@zitnick2014edge] to propose candidate ones, which also reduces background noise. We set $k$=10 in our experiments.

[**Clustering into Subcategories**]{}: We then use a publicly-available variant of agglomerative clustering [@chen_cvpr14] where the nearest neighbor sets are merged iteratively from bottom up to form the final subcategories based on E-LDA similarity scores and density estimation. Note that this is different from [@chen2013neil], but gives similar results while being much more efficient. Some example subcategories are shown in Figure \[fig:subcat\].

![image](rel){width="1.0\linewidth"}

Finally, we train a R-CNN [@girshick2014rich] detector for each category based on all the clustered bounding boxes. Random patches from YFCC [@yfcc] are used as negatives. The naive approach would be using the positive examples as-is. Typically, hundreds of instances per category are available for training. While this number is comparable to the VOC 2007 trainval set [@everingham2010pascal], we also tried to increase positive bounding boxes using two strategies:

[**EdgeBox Augmentation (EA)**]{}: We follow [@girshick2014rich] to augment the positive training examples. We again use EdgeBox [@zitnick2014edge] to propose regions of interest on images. Whenever a proposal has a $\ge$0.5 overlapping (measured by intersection over union) with any of the positive bounding 