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A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging

机译：统一协作和基于内容的图像标记的混合概率模型

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摘要

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 L_1 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.
机译：带有标签的大量用户贡献图像的可用性不断提高，为开发自动标记图像的工具提供了机会，以利于图像搜索和检索。在本文中，我们提出了一种新颖的混合概率模型（HPM），该模型集成了低级图像特征和高级用户提供的标签以自动标记图像。对于没有标签的图像，HPM仅根据低级图像功能预测新标签。对于带有用户提供的标签的图像，HPM会在统一的概率框架中共同利用图像功能和标签，以推荐其他标签来标记图像。 HPM框架利用了标签图像关联矩阵（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种多样的，因此TIAM非常稀疏，因此难以可靠地估计标签到标签的共现概率。我们开发了基于非负矩阵分解（NMF）的协作过滤方法来解决此数据稀疏性问题。此外，使用L_1范数核方法来估计图像特征与语义概念之间的相关性。使用三个数据库分别评估了所提出方法的有效性，该数据库包含具有371个标签的5,000个图像，具有5,587个标签的31,695个图像和具有5,018个标签的269,648个图像。

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