Multi-label learning with label relevance in advertising video

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Journal Article
Neurocomputing, 2016, 171 pp. 932 - 948
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The recent proliferation of videos has brought out the need for applications such as automatic annotation and organization. These applications could greatly benefit from the respective thematic content depending on the type of video. Unlike the other kinds of video, an advertising video usually conveys a specific theme in a certain time period (e.g. drawing the audience׳s attention to a product or emphasizing the brand). Traditional multi-label algorithms may not work effectively with advertising videos due mainly to their heterogeneous nature. In this paper, we propose a new learning paradigm to resolve the problems arising out of traditional multi-label learning in advertising videos through label relevance. Aiming to address the issue of label relevance, we firstly assign each label with label degree (LD) to classify all the labels into three groups such as first label (FL), important label (IL) and common label (CL), and then propose a Directed Probability Label Graph (DPLG) model to mine the most related labels from the multi-label data with label relevance, in which the interdependency between labels is considered. In the implementation of DPLG, the labels that appear occasionally and possess inconspicuous co-occurrences are consequently eliminated effectively, employing λ-filtering and τ-pruning processes, respectively. And then the graph theory is utilized in DPLG to acquire Correlative Label-Sets (CLSs). Lastly, the searched Correlative Label-Sets (CLSs) are utilized to enhance multi-label annotation. Experimental results on advertising videos and several publicly available datasets demonstrate the effectiveness of the proposed method for multi-label annotation with label relevance
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