One Shot Logo Recognition Based on Siamese Neural Networks

Abstract

This work presents an approach for one-shot logo recognition that relies on a Siamese neural network (SNN) embedded with a pre-trained model that is fine-tuned on a challenging logo dataset. Although the model is fine-tuned using logo images, the training and testing datasets do not have overlapped categories; meaning that, all the classes used for testing the one-shot recognition framework remain unseen during the fine-tuning process. The recognition process follows the standard SNN approach in which a pair of input images are encoded by each sister network. The encoded outputs for each image are afterwards compared using a trained metric and thresholded to define matches and mismatches. The proposed approach achieves an accuracy of 77.07% under the one-shot constraints in the QMUL-OpenLogo dataset. Code is available at https://github.com/cjvargasc/oneshot_siamese/.