Topic regression multi-modal Latent Dirichlet Allocation for image annotation

The proposed association model shows improved performance over correspondence LDA as measured by caption perplexity, and a novel latent variable regression approach to capture correlations between image or video features and annotation texts.

Abstract

We present topic-regression multi-modal Latent Dirich-let Allocation (tr-mmLDA), a novel statistical topic model for the task of image and video annotation. At the heart of our new annotation model lies a novel latent variable regression approach to capture correlations between image or video features and annotation texts. Instead of sharing a set of latent topics between the 2 data modalities as in the formulation of correspondence LDA in [2], our approach introduces a regression module to correlate the 2 sets of topics, which captures more general forms of association and allows the number of topics in the 2 data modalities to be different. We demonstrate the power of tr-mmLDA on 2 standard annotation datasets: a 5000-image subset of COREL and a 2687-image LabelMe dataset. The proposed association model shows improved performance over correspondence LDA as measured by caption perplexity.