Image annotation based on multiple feature tag relevance learning

An image annotation method was proposed based on multiple feature tag relevance learning (MFTRL), which aimed at tagging large-scale image collections in real environment by analyzing the correlation between tags and images represented by multiple visual features. First, a multiple label learning method was utilized to generate the relevance of tags and images in specific feature space. Then, an optimal threshold was set for each tag and corresponding single feature. So the output of many tag relevance learners driven by diverse features could be combined in the manner of combining multi- feature tag relevance. The experiments over the internet image set demonstrate that the proposed method is accurate and stable.