Estimation of the frequency and duration of logos in videos is important and challenging in the advertisement industry as a way of estimating the impact of ad purchases. Since logos occupy only a small area in the videos, the popular methods of image retrieval could fail. This paper develops an algorithm called Video Logo Retrieval (VLR), which is an image-to-video retrieval algorithm based on the spatial distribution of local image descriptors that measure the distance between the query image (the logo) and a collection of video images. VLR uses local features to overcome the weakness of global feature-based models such as convolutional neural networks (CNN). Meanwhile, VLR is flexible and does not require training after setting some hyper-parameters. The performance of VLR is evaluated on two challenging open benchmark tasks (SoccerNet and Standford I2V), and compared with other state-of-the-art logo retrieval or detection algorithms. Overall, VLR shows significantly higher accuracy compared with the existing methods.
@article{arxiv.1808.03735,
title = {Video Logo Retrieval based on local Features},
author = {Bochen Guan and Hanrong Ye and Hong Liu and William A. Sethares},
journal= {arXiv preprint arXiv:1808.03735},
year = {2020}
}
Comments
Accepted by ICIP 20. Contact author: Bochen Guan (gbochen@wisc.edu)