Scalable Logo Recognition using Proxies

srikar appalaraju

Abstract:

Logo recognition is the task of identifying and classifying
logos. Logo recognition is a challenging problem as there
is no clear definition of a logo and there are huge variations
of logos, brands and re-training to cover every variation is
impractical. In this paper, we formulate logo recognition as
a few-shot object detection problem. The two main components in our pipeline are universal logo detector and fewshot logo recognizer. The universal logo detector is a classagnostic deep object detector network which tries to learn
the characteristics of what makes a logo. It predicts bounding boxes on likely logo regions. These logo regions are then
classified by logo recognizer using nearest neighbor search,
trained by triplet loss using proxies. We also annotated a
first of its kind product logo dataset containing 2000 logos from 295K images collected from Amazon called PL2K.
Our pipeline achieves 97% recall with 0.6 mAP on PL2K
test dataset and state-of-the-art 0.565 mAP on the publicly
available FlickrLogos-32 test set without fine-tuning.