Open Set Logo Detection and Retrieval

TL;DR

This paper introduces an open set logo detection and retrieval framework that can find unseen logos in images or videos, supported by a new large-scale dataset and a two-stage CNN-based method, outperforming closed set approaches.

Contribution

It proposes the first open set logo retrieval approach with a dedicated dataset, enabling detection of unseen logos using a two-stage CNN system.

Findings

Significant performance improvements over closed set methods.

Effective detection of unseen logos with a single query.

Large-scale Logos in the Wild dataset facilitates open set logo research.

Abstract

Current logo retrieval research focuses on closed set scenarios. We argue that the logo domain is too large for this strategy and requires an open set approach. To foster research in this direction, a large-scale logo dataset, called Logos in the Wild, is collected and released to the public. A typical open set logo retrieval application is, for example, assessing the effectiveness of advertisement in sports event broadcasts. Given a query sample in shape of a logo image, the task is to find all further occurrences of this logo in a set of images or videos. Currently, common logo retrieval approaches are unsuitable for this task because of their closed world assumption. Thus, an open set logo retrieval method is proposed in this work which allows searching for previously unseen logos by a single query sample. A two stage concept with separate logo detection and comparison is proposed where both modules are based on task specific CNNs. If trained with the Logos in the Wild data, significant performance improvements are observed, especially compared with state-of-the-art closed set approaches.