Multimedia automatic annotation by mining label set correlation

Organizing and retrieving multimedia data heavily rely on the relevant textual descriptions. Multimedia automatic annotation, which assigns text labels to multimedia samples, has been widely studied. Among others, search-based annotation methods are well suited for annotation tasks on large-scale datasets and are studied in depth because of their simplicity and scalability. However, classical search based annotation methods address this problem by treating each label independently, which ignores the correlation between different labels in the assigned label set. This paper aims to integrate the relevant information of the label set with respect to the multimedia content and the inner correlated information of the label set into a joint learning framework. We evaluate the performance of the proposed method on MIRFLICKR-25000 and NUS-WIDE datasets. Experimental results show that the proposed annotation method achieves excellent performance.