Sparse Multi-Modal Topical Coding for Image Annotation

NEUROCOMPUTING

卷:214

Article

Song, Lingyun¹  Luo, Minnan¹  Liu, Jun¹  Zhang, Lingling¹  Qian, Buyue¹  Li, Max Haifei²  Zheng, Qinghua¹ 

[1] Xi An Jiao Tong Univ, Dept Comp Sci & Technol, SPKLSTN Lab, Xian 710049, Peoples R China

[2] Union Univ, Dept Comp Sci, Jackson, TN 38305 USA

关键词: Topic models;    Sparse latent representation;    Image annotation;    Image retrieval;   

DOI  :  10.1016/j.neucom.2016.06.005

来源: Elsevier

【 摘 要 】

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. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

Free