Image annotation plays a significant role in large scale image understanding, indexing and retrieval. The Probability Topic Models (PTMs) attempt to address this issue by learning latent representations of input samples, and have been shown to be effective by existing studies. Though useful, PTM has some limitations in interpreting the latent representations of images and texts, which if addressed would broaden its applicability. In this paper, we introduce sparsity to PTM to improve the interpretability of the inferred latent representations. Extending the Sparse Topical Coding that originally designed for unimodal documents learning, we propose a non-probabilistic formulation of PTM for automatic image annotation, namely Sparse Multi-Modal Topical Coding. Beyond controlling the sparsity, our model can capture more compact correlations between words and image regions. Empirical results on some benchmark datasets show that our model achieves better performance on automatic image annotation and text-based image retrieval over the baseline models.
['Lingyun Song', 'Minnan Luo', 'Jun Liu', 'Lingling Zhang', 'Buyue Qian', 'Max Haifei Li', 'Qinghua Zheng']
Sparse Multi-Modal Topical Coding for Image Annotation