Multi-label learning with label relevance in advertising video.

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. [ABSTRACT FROM AUTHOR]