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CoMMA: a framework for integrated multimedia mining using multi-relational associations

机译：CoMMA：使用多关系关联的集成多媒体挖掘的框架

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摘要

Generating captions or annotations automatically for still images is a challenging task. Traditionally, techniques involving higher-level (semantic) object detection and complex feature extraction have been employed for scene understanding. On the basis of this understanding, corresponding text descriptions are generated for a given image. In this paper, we pose the auto-annotation problem as that of multi-relational association rule mining where the relations exist between image-based features, and textual annotations. The central idea is to combine low-level image features such as color, orientation, intensity, etc. and corresponding text annotations to generate association rules across multiple tables using multi-relational association mining. Subsequently, we use these association rules to auto-annotate test images. In this paper we also present a multi-relational extension to the FP-tree algorithm to accomplish the association rule mining task effectively. The motivation for using multi-relational association rule mining for multimedia data mining is to exhibit the potential accorded by multiple descriptions for the same image (such as multiple people labeling the same image differently). Moreover, multi-relational association rule mining can also benefit the auto-annotation process by pruning the number of trivial associations that are generated if text and image features were combined in a single table through a join. In this paper, we discuss these issues and the results of our auto-annotation experiments on different test sets. Another contribution of this paper is highlighting a need to develop robust evaluation metrics for the image annotation task. We propose several applicable scoring techniques and then evaluate the performance of the different algorithms to study the utility of these techniques. A detailed analysis of the datasets used and the performance results are presented to conclude the paper.
机译：为静止图像自动生成标题或注释是一项艰巨的任务。传统上，涉及高级（语义）对象检测和复杂特征提取的技术已用于场景理解。基于该理解，针对给定图像生成相应的文本描述。在本文中，我们提出了自动注释问题，即多关系关联规则挖掘的问题，其中基于图像的特征与文本注释之间存在关系。中心思想是将低级图像特征（例如颜色，方向，强度等）和相应的文本注释结合起来，以使用多关系关联挖掘在多个表之间生成关联规则。随后，我们使用这些关联规则来自动注释测试图像。在本文中，我们还提出了对FP-tree算法的多关系扩展，以有效地完成关联规则挖掘任务。使用多关系关联规则挖掘进行多媒体数据挖掘的动机是展现针对同一图像的多种描述（例如，多个人以不同的方式标记同一图像）所具有的潜力。此外，多文本关联规则挖掘还可以通过修剪文本和图像特征通过联接组合在单个表中而生成的琐碎关联数，从而从自动注释过程中受益。在本文中，我们讨论了这些问题以及在不同测试集上进行自动注释实验的结果。本文的另一个贡献是强调需要为图像注释任务开发鲁棒的评估指标。我们提出了几种适用的评分技术，然后评估了不同算法的性能，以研究这些技术的实用性。本文对所使用的数据集和性能结果进行了详细分析，以得出结论。

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