7,984 research outputs found
Open Set Logo Detection and Retrieval
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.Comment: accepted at VISAPP 201
Exploiting multimedia in creating and analysing multimedia Web archives
The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general
Multimedia information technology and the annotation of video
The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning
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