Cross-media Topic Detection with Refined CNN based Image-Dominant Topic Model

Online heterogenous data is springing up while the data has the rich auxiliary information (e.g. pictures and videos) around the text. However, traditional topic models are suffering from the limitations to discover the topics effectively from the cross-media data. Incorporating with the convolutional neural network (CNN) feature, we propose a novel image dominant topic model, which projects both the text modality and the visual modality into a semantic simplex. Further, an improved CNN feature is introduced to capture more visual details by fusing the convolutional layer and fully-connected layer. Experimental comparisons with state-of-the-art methods in the cross-media topic detection task show the effectiveness of our model.