Symbiosis between the TRECVid benchmark and video libraries at the Netherlands Institute for Sound and Vision

In this paper, we invest the domain transfer learning problem with multi-instance data. We assume we already have a well-trained multi-instance dictionary and its corresponding classifier from the source domain, which can be used to represent and classify the bags. But it cannot be directly used to the target domain. Thus we propose to adapt them to the target domain by adding an adaptive term to the source domain classifier. The adaptive function is a linear function based on a domain transfer multi-instance dictionary. Given a target domain bag, we first map it to a bag-level feature space using the domain transfer dictionary and then apply a linear adaptive function to its baglevel feature vector. To learn the domain transfer dictionary and the adaptive function parameter, we simultaneously minimize the average classification error of the target domain classifier over the target domain training set, and the complexities of both the adaptive function parameter and the domain transfer dictionary. The minimization problem is solved by an iterative algorithm which updates the dictionary and the function parameter alternately. Experiments over several benchmark data sets show the advantage of the proposed method over existing stateof-the-art domain transfer multi-instance learning methods.

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“…The first data set used is the TRECVID data set [26,1]. This data set is a set of key frames of video programs.…”

Section: Trecvid Data Setmentioning

confidence: 99%

Domain transfer multi-instance dictionary learning

Wang

Liu

González

2016

Neural Comput & Applic

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“…Another important benefit of benchmarks it that they provide stakeholder, e.g., companies who are interested in developing products based on placing technology, with a way to overview and to compare different solutions. Involving stakeholders in benchmarking can couple technologies to real-world needs [45].…”

Section: The Benefits Of Benchmarkingmentioning

confidence: 99%