基于多标签学习的图像语义自动标注研究
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摘要
随着多媒体数字化技术的发展和推广、存储成本的降低、网络传输带宽的增长,各种多媒体数据如图像、视频等飞速膨胀逐渐成为信息的主流,并对人们的生活和社会发展产生重要的影响。“语义清晰”是大规模多媒体数据管理的重要前提,因此通过信息技术自动获取多媒体数据对象的语义内容的研究具有十分重要的理论与实践意义,引起了学术界与工业界的高度关注。
图像是视频的基础,在多媒体数据管理中占有重要的地位,因此图像语义的自动标注技术是当前相关领域的研究热点。图像语义的自动标注本质上是一个“学习”问题,即根据图像的视觉内容推导出图像的语义标签。因此,各种机器学习、统计推理技术都应用于图像标注的研究中,并在不断的深化和推进。然而,由于图像标注中“语义鸿沟”以及“多标签”问题的影响,现有方法的标注性能仍有待进一步提高。
本文围绕图像标注的多标签特点,集中利用多标签相关性,对多标签带来的数据重叠、数据不平衡等问题以及Web图像标注开展研究,在基于生成模型的多标签传递、生成模型与判别分类方法相结合的图像标注、基于噪声训练集的Web图像标注等方面进行了新的尝试,提出多个具有较好性能的图像标注方法。
本文主要研究内容如下:
1.提出扩展生成模型的图像标注方法:为了有效利用多标签之间的相关性,将原始生成模型扩展为对多标签同时标注,并提出启发式迭代算法进行求解。在该方法中,提出主题-图像-区域多粒度层次特征估计模型,对语义关键词之间的相关性进行分析,并使两者在提出的迭代算法中相互结合共同改进标注性能。实验证明所提基于扩展生成模型的图像标注方法较传统生成模型在标注准确度上有明显改进。
2.提出基于可判别超平面树的图像标注方法:基于待标注图像的高视觉生成领域构造局部隐藏主题层次结构,并在其基础上构造可判别超平面树。在引入分类器的判别能力的同时,保留了基于概率模型的图像语义标注的优点,实现将生成模型与判别分类方法相结合改进图像标注。实验证明所提基于可判别超平面树的图像标注方法较之传统生成模型和判别分类模型在标注准确度上有明显提高。
3.提出基于局部多标签分类的图像标注方法:给出将生成模型与判别分类技术相结合用于图像标注的另一个解决思路,更深层次的考虑并区分特征相似所隐含的不同语义模式,并对多标签语义特征空间及特征空间的分类边界同时进行考虑,以使生成的隐藏主题同时获得较大的语义和视觉可分性。实验证明所提基于局部多标签分类的图像标注方法较之传统生成模型和判别分类模型在标注准确度上有明显提高。
4.提出基于噪声训练集的Web图像标注方法:本文给出一个完整的Web图像标注解决方案。首先提出一个自动生成Web图像标注训练集的“轻量级”方法,进而针对训练集中的噪声数据,设计基于混合模型局部Fisher判别分析的Web图像标注方法。实验表明所提标注方法在存在噪声数据的情况下较传统标注方法获得较好的标注效果。
With the rapid development and widespread of multimedia digital techniques, the reduction in storage cost and the transmission bandwidth growth of the network, multimedia information such as image and video become ever more available, and play big role in people's life and social development. "Explicit semantics" is an important prerequisite for large scale multimedia information management. So automatically obtaining the semantics of the multimedia data by using information techniques has important meaning in theory and practice, and attracts great attentions in academic and industrial fields.
Image is the basis of the video. It occupies an important position in multimedia data management. So automatic image annotation (AIA) is the hot research issue in the related fields. The nature of AIA is a process of "learning", that is, associating images with semantic keywords according to their visual contents. Thus, machine learning methods and statistical inference techniques have both been applied to solving the problem of AIA, which is continuously deepening and promoting. However, due to the problem of "semantic gap" and multi-labeling, the annotation performance of existing methods is not satisfactory, and needs to be further improved.
This paper studies multi-label characteristic of AIA. By concentrating on the correlation between multiple keywords, we addresses the problems of data imbalance and overlapping brought about by multi-labeling and Web image annotation problem. Based on this, several image annotation methods with good performance are proposed, which are mainly about generative model based multiple class label propagation, the combined generative model and discriminative techniques and noisy training set based web image annotation.
The main work of this paper is as follows:
1. A new image annotation method via extended generative model is proposed: inorder to exploit the correlation between keywords, we propose a new image annotation method by extending the tradition generative model to estimating the probability of a set of keywords being the caption of an image, and present a heuristic iterative algorithm to solve the problem. In this method, we propose a topic-image-region multi-granular hierarchical feature estimation model, and analyze the correlation between keywords. Both of their contributions to image annotation are extensively exerted according to our heuristic iterative algorithm. The experimental results on a real world benchmark show that our method outperforms the traditional generative model based annotation method.
2. The discriminative hyperplane tree based image annotation method is proposed: this method leverages the benefits of the generative and discriminative models by building the local latent topic hierarchy and the corresponding hyperplane tree based on the high generative probability neighborhood of the unlabel image. The experimental results on a real world benchmark show that our method outperforms the state-of-the-art generative model based annotation method and discriminative model based method.
3. The local multi-label classification based image annotation method is proposed:this method provides another solution to combine generative model and discriminative techniques to improve AIA. We further explore the underlying semantics of visual similarities, and try to find the optimal margin in both visual and semantic spaces when generating the latent topic to obtain large separation in both spaces. The experimental results on a real world benchmark show that our method outperforms the state-of-the-art generative model based annotation method and discriminative model based method.
3. A new Web image annotation method based on noisy training set is proposed:we present a novel web annotation framework. We introduce a "light weight" method to obtain the training set automatically. Then, we propose a novel annotation method based on mixture component based local fisher discriminant analysis to deal with the bad influence of the noisy training data. The experimental results on a real world Web image data set show that our method outperforms the traditional annotation approaches with noisy training data.
图像是视频的基础,在多媒体数据管理中占有重要的地位,因此图像语义的自动标注技术是当前相关领域的研究热点。图像语义的自动标注本质上是一个“学习”问题,即根据图像的视觉内容推导出图像的语义标签。因此,各种机器学习、统计推理技术都应用于图像标注的研究中,并在不断的深化和推进。然而,由于图像标注中“语义鸿沟”以及“多标签”问题的影响,现有方法的标注性能仍有待进一步提高。
本文围绕图像标注的多标签特点,集中利用多标签相关性,对多标签带来的数据重叠、数据不平衡等问题以及Web图像标注开展研究,在基于生成模型的多标签传递、生成模型与判别分类方法相结合的图像标注、基于噪声训练集的Web图像标注等方面进行了新的尝试,提出多个具有较好性能的图像标注方法。
本文主要研究内容如下:
1.提出扩展生成模型的图像标注方法:为了有效利用多标签之间的相关性,将原始生成模型扩展为对多标签同时标注,并提出启发式迭代算法进行求解。在该方法中,提出主题-图像-区域多粒度层次特征估计模型,对语义关键词之间的相关性进行分析,并使两者在提出的迭代算法中相互结合共同改进标注性能。实验证明所提基于扩展生成模型的图像标注方法较传统生成模型在标注准确度上有明显改进。
2.提出基于可判别超平面树的图像标注方法:基于待标注图像的高视觉生成领域构造局部隐藏主题层次结构,并在其基础上构造可判别超平面树。在引入分类器的判别能力的同时,保留了基于概率模型的图像语义标注的优点,实现将生成模型与判别分类方法相结合改进图像标注。实验证明所提基于可判别超平面树的图像标注方法较之传统生成模型和判别分类模型在标注准确度上有明显提高。
3.提出基于局部多标签分类的图像标注方法:给出将生成模型与判别分类技术相结合用于图像标注的另一个解决思路,更深层次的考虑并区分特征相似所隐含的不同语义模式,并对多标签语义特征空间及特征空间的分类边界同时进行考虑,以使生成的隐藏主题同时获得较大的语义和视觉可分性。实验证明所提基于局部多标签分类的图像标注方法较之传统生成模型和判别分类模型在标注准确度上有明显提高。
4.提出基于噪声训练集的Web图像标注方法:本文给出一个完整的Web图像标注解决方案。首先提出一个自动生成Web图像标注训练集的“轻量级”方法,进而针对训练集中的噪声数据,设计基于混合模型局部Fisher判别分析的Web图像标注方法。实验表明所提标注方法在存在噪声数据的情况下较传统标注方法获得较好的标注效果。
With the rapid development and widespread of multimedia digital techniques, the reduction in storage cost and the transmission bandwidth growth of the network, multimedia information such as image and video become ever more available, and play big role in people's life and social development. "Explicit semantics" is an important prerequisite for large scale multimedia information management. So automatically obtaining the semantics of the multimedia data by using information techniques has important meaning in theory and practice, and attracts great attentions in academic and industrial fields.
Image is the basis of the video. It occupies an important position in multimedia data management. So automatic image annotation (AIA) is the hot research issue in the related fields. The nature of AIA is a process of "learning", that is, associating images with semantic keywords according to their visual contents. Thus, machine learning methods and statistical inference techniques have both been applied to solving the problem of AIA, which is continuously deepening and promoting. However, due to the problem of "semantic gap" and multi-labeling, the annotation performance of existing methods is not satisfactory, and needs to be further improved.
This paper studies multi-label characteristic of AIA. By concentrating on the correlation between multiple keywords, we addresses the problems of data imbalance and overlapping brought about by multi-labeling and Web image annotation problem. Based on this, several image annotation methods with good performance are proposed, which are mainly about generative model based multiple class label propagation, the combined generative model and discriminative techniques and noisy training set based web image annotation.
The main work of this paper is as follows:
1. A new image annotation method via extended generative model is proposed: inorder to exploit the correlation between keywords, we propose a new image annotation method by extending the tradition generative model to estimating the probability of a set of keywords being the caption of an image, and present a heuristic iterative algorithm to solve the problem. In this method, we propose a topic-image-region multi-granular hierarchical feature estimation model, and analyze the correlation between keywords. Both of their contributions to image annotation are extensively exerted according to our heuristic iterative algorithm. The experimental results on a real world benchmark show that our method outperforms the traditional generative model based annotation method.
2. The discriminative hyperplane tree based image annotation method is proposed: this method leverages the benefits of the generative and discriminative models by building the local latent topic hierarchy and the corresponding hyperplane tree based on the high generative probability neighborhood of the unlabel image. The experimental results on a real world benchmark show that our method outperforms the state-of-the-art generative model based annotation method and discriminative model based method.
3. The local multi-label classification based image annotation method is proposed:this method provides another solution to combine generative model and discriminative techniques to improve AIA. We further explore the underlying semantics of visual similarities, and try to find the optimal margin in both visual and semantic spaces when generating the latent topic to obtain large separation in both spaces. The experimental results on a real world benchmark show that our method outperforms the state-of-the-art generative model based annotation method and discriminative model based method.
3. A new Web image annotation method based on noisy training set is proposed:we present a novel web annotation framework. We introduce a "light weight" method to obtain the training set automatically. Then, we propose a novel annotation method based on mixture component based local fisher discriminant analysis to deal with the bad influence of the noisy training data. The experimental results on a real world Web image data set show that our method outperforms the traditional annotation approaches with noisy training data.
引文
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