Open Set Logo Detection and Retrieval
This work addresses the need for open set logo detection and retrieval, which is crucial for applications like assessing advertisement effectiveness in sports broadcasts, but it is incremental as it builds on existing CNN methods.
The paper tackles the problem of logo retrieval in open set scenarios, where existing methods fail due to closed world assumptions, by proposing a two-stage CNN-based method and releasing a large-scale dataset called Logos in the Wild, resulting in significant performance improvements over state-of-the-art closed set approaches.
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.