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Towards large-scale multimedia retrieval enriched by knowledge about human interpretation

机译：致力于通过丰富的人类解释知识来进行大规模的多媒体检索

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Recent Large-Scale Multimedia Retrieval (LSMR) methods seem to heavily rely on analysing a large amount of data using high-performance machines. This paper aims to warn this research trend. We advocate that the above methods are useful only for recognising certain primitive meanings, knowledge about human interpretation is necessary to derive high-level meanings from primitive ones. We emphasise this by conducting a retrospective survey on machine-based methods which build classifiers based on features, and human-based methods which exploit user annotation and interaction. Our survey reveals that due to prioritising the generality and scalability for large-scale data, knowledge about human interpretation is left out by recent methods, while it was fully used in classical methods. Thus, we defend the importance of human-machine cooperation which incorporates the above knowledge into LSMR. In particular, we define its three future directions (cognition-based, ontology-based and adaptive learning) depending on types of knowledge, and suggest to explore each direction by considering its relation to the others.
机译：最近的大型多媒体检索（LSMR）方法似乎严重依赖使用高性能计算机分析大量数据。本文旨在警告这一研究趋势。我们主张上述方法仅对识别某些原始含义有用，有关人类解释的知识对于从原始含义中获取高级含义是必需的。我们通过对基于机器的基于特征构建分类器的方法以及利用用户注释和交互的基于人的方法进行回顾性调查来强调这一点。我们的调查显示，由于优先考虑大型数据的通用性和可伸缩性，因此有关人类解释的知识在最近的方法中被遗漏了，而在经典方法中已被完全使用。因此，我们捍卫了将上述知识纳入LSMR的人机合作的重要性。特别是，我们根据知识的类型来定义其未来的三个方向（基于认知，基于本体和自适应学习），并建议通过考虑每个方向与其他方向的关系来探索每个方向。

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