Tag Pollution Detection in Web Videos via Cross-Modal Relevance Estimation

In the era of big data, web videos are known for their astronomical volume and great difficulties to be understood by computers. Therefore it is challenging to detect and curb tag pollution on social networking platforms. Intuitively, the pollution in the tags of a video can be identified by exploiting the tags of visually similar videos. From this intuition, we develop a semi-supervised approach to estimate the relevance between Internet videos and user-provided labels accurately and detect polluted labels accordingly. To further enhance the accuracy of relevance estimation and pollution detection, we introduce three multi-view multi-label models, which employ the coherence and differences between various similarity relations of videos. Compared with two quintessential multi-view fusion models, the proposed models consistently outperform or achieve comparable performance.