A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging
A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging
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DOI：
10.1109/tpami.2010.204
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发表时间：
2011-07
影响因子：
23.6
通讯作者：
Ning Zhou;W. K. Cheung;G. Qiu;X. Xue
中科院分区：
文献类型：
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作者：
Ning Zhou;W. K. Cheung;G. Qiu;X. Xue
大量用户贡献的带有标签的图像的日益增加的可用性提供了开发自动工具来标记图像以促进图像搜索和检索的机会。在本文中，我们提出了一种新的混合概率模型（HPM），它集成了低级别的图像特征和高级别的用户提供的标签自动标记图像。对于没有任何标签的图像，HPM仅基于低级图像特征预测新标签。对于具有用户提供的标签的图像，HPM在统一的概率框架中联合利用图像特征和标签来推荐额外的标签来标记图像。HPM框架使用了标记图像关联矩阵（TIAM）。然而，由于图像的数量通常非常大，并且用户提供的标签是多样的，TIAM非常稀疏，从而使得难以可靠地估计标签到标签的共现概率。我们开发了一种基于非负矩阵分解（NMF）的协同过滤方法来解决这个数据稀疏性问题。此外，L1范数核方法被用来估计图像特征和语义概念之间的相关性。所提出的方法的有效性进行了评估，使用三个数据库包含5,000图像371标签，31,695图像5,587标签，269,648图像5,018标签，分别。
The increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level image features and high-level user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based solely on the low-level image features. For images with user provided tags, HPM jointly exploits both the image features and the tags in a unified probabilistic framework to recommend additional tags to label the images. The HPM framework makes use of the tag-image association matrix (TIAM). However, since the number of images is usually very large and user-provided tags are diverse, TIAM is very sparse, thus making it difficult to reliably estimate tag-to-tag co-occurrence probabilities. We developed a collaborative filtering method based on nonnegative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The effectiveness of the proposed approach has been evaluated using three databases containing 5,000 images with 371 tags, 31,695 images with 5,587 tags, and 269,648 images with 5,018 tags, respectively.