Sparse Multi-Modal Topical Coding for Image Annotation

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• 作者：Lingyun Song^a ; ^{lingyun.a.song@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Minnan Luo^a ; ^{minnluo@mail.xjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jun Liu^a ; ^{liukeen@mail.xjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Lingling Zhang^a ; ^{linglingzhang0929@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Buyue Qian^a ; ^{qianbuyue@mail.xjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Max Haifei Li^b ; ^{hli@uu.edu" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Qinghua Zheng^a ; ^{qhzheng@mail.xjtu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Topic models ; Sparse latent representation ; Image annotation ; Image retrieval
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：19 November 2016
• 年：2016
• 卷：214
• 期：Complete
• 页码：162-174
• 全文大小：6778 K

文摘

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.