视觉语言分析：从底层视觉特征表达到语义距离学习
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摘要
随着互联网的发展,网络图像资源与日剧增,伴随而来的是围绕着网络图像为对象的众多研究课题,比如图像标注、图像检索、图像搜索结果聚类、重复图像检测、图像标签推荐、图像索引、图像分类、物体检测等。这些相关研究都需要从根本上解决一个问题,即视觉语义的表达和度量。因此该问题成为了网络图像研究的一个基本和核心的问题,也是目前学术界和工业界的重要研究方向。
目前来看,视觉语义的表达和度量主要包含四个基本问题,即图像表达、图像相似度量、概念表达和概念相关度量。图像表达是指图像的特征以及特征的组织形式。图像特征可以生成有一定分辨能力的视觉单词、视觉短语。图像的表达形式也是多种多样,比如有忽略特征间关系的,有考虑在一幅图像中空间关系的或者共发频率的,等等。图像的距离度量是在特定图像表达之上通过机器学习的方法得到的。选择不同的图像特征和图像表达,图像的距离度量或许不一样。同样,概念表达是指概念的特征及其组织形式。概念的特征是以众多包含某个概念的图像集合上提取的视觉特征为基础建立的模型,它的通常形式是某个视觉单词或者短语出现的频率、共发频率、条件分布、或者转移概率。目前有很多概念建模的方法,比如二维隐马尔可夫模型、条件随机场模型等。本文的后一部分章节将详细讨论我们提出的视觉语言模型,保语义单词包模型,并和其他各种模型进行比较。概念距离度量是建立在特定的概念表达模型或者文本语义关联之上的概念之间的相关性度量。目前常用的度量方法包括词网距离、谷歌距离、以及我们提出的Flickr距离。
本文提出了解决视觉语义的表达和度量的一系列模型和方法,既有底层特征的创新,也有高层语义模型和距离度量方面的创新。发表的工作涉及了以上提及的四个方面挑战,为视觉语义的表达和度量相关研究提供了有意义的探索。具体来说本文的成果和创新之处包括以下几点：
1.本文提出了视觉语言模型,减小了视觉领域和文本领域的语义分析的鸿沟。我们认为图像的局部特征和文本中的单词一样是满足一定的语法顺序的。利用计算这些局部特征在空间位置上的条件分布来表达这种语法顺序,就可以近似的估测图像中的视觉语义。因为该模型和文本分析中的自然语言模型形式上类似,因此很多文本分析中的方法可以很容易在该模型中推广。实验结果表明该模型效果和很多复杂的模型相近,但运算速度远远超过其它模型,可以很好的应用在大规模数据上。
2.本文提出了保语义单词包模型来处理语义鸿沟问题。我们提出了一种语义鸿沟度量方法,并通过选择从视觉特征到视觉单词的映射空间来最小化语义鸿沟,从而使得我们产生的词典可以有更好的分辨能力。实验也证明了利用最小化语义鸿沟方法生成的词典在图像标注问题中效果明显优于其他方法。
3.本文提出了概率相关成分分析方法用来改进图像相似度量。概率相关成分分析将图像之间的边信息表达为概率的形式而不是传统的非0即1的二进制表达,提高了图像距离学习的精度。网络图像标注的应用显示出该方法比传统的距离学习方法更加高效和准确。
4.本文提出了基于视觉特征的概念相关性度量方法：Flickr距离。该距离可以用来度量两个概念的不相关度。我们认为相关的概念同时出现在同一幅图像中概率比较大。因此计算和两个概念分别相关的图像的视觉语言模型的差别,就可以有效地度量概念之间的不相关度。和其它基于文本的概念距离度量方法不同,Flickr距离应用了概念相关的图片信息,从视觉角度度量了概念的相关性。在多媒体相关的应用问题中可以显著地提高性能。和人工建立的词网距离比较,Flickr距离可以自动更新以覆盖更多的新概念,和传统的谷歌距离比较,Flickr距离利用了视觉信息,实验证明其更加符合人类的认知。
5.本文将传统的线性空间距离学习推广到了非线性空间距离,提出了Bregman距离函数学习方法。传统的Mahalanobis距离学习是需要学习一个距离矩阵。该距离度量在整个空间中是一致的。而样本在空间的分布疏密可能是有差别的。利用Bregma距离学习可能得到一个和样本相关的度量,考虑了局部分布的特性,因此可能更加准确。实验表明该方法可以比其他方法更好地处理高维空间的距离学习问题。
6.本文将传统的静态距离推广到了动态距离,提出了QOSS子空间选取方法。我们认为观测角度(度量空间)不同,对样本的距离度量会产生很大的影响。因此在判断两个样本是否相近的时候,在多个子空间中度量比在单个子空间度量要准确。我们提出了根据样本特性,自动选取多个子空间的策略对样本相似性进行度量。在网络图像近似重复检测中,我们发现经过不超过5轮迭代,检测精度可以显著提高。
With the development of Internet, there is proliferation of Web image resources. Lots of research problems come along with Web images, such as image annotation, image retrieval, search result clustering, near-duplicate detection, image tag recom-mendation, image indexing, image classification, object detection, etc. All these search topics have to deal with one intrinsic and fundamental problem, that is visual seman-tic representation and measurements. Thus this problem has become a hot research direction for both academy and industry.
Currently, visual semantic representation and measurements can be divided into four elements, including image representation, image similarity measurement, concept representation and concept correlation measurement. Image representation refers to im-age feature and the arrangement of features. The feature types could be various and so is the arrangements. Image similarity measurement is based on the image representa-tion, and could be generated by machine learning technology. Similarity measurement could be quite different by choosing different image features and the representations. Concept representation refers to the features of concept and their arrangement. The features of concept can be generated from a collection of images related to the concept. Currently there are lots of successful concept modeling methods, such as 2D hidden Markov model, conditional random fields, etc. Comparing to these complex models, we propose the visual language model, which is simple and effective. We also propose the semantic preserving bag of words model to help solve the semantic gap problem. The correlation between concepts is based on the concept models. As far as we know, there are several concept distance measurements, such as WordNet distance, google distance, etc. These distance are based on human labor or text information, while our proposed Flickr distance measures the concept distance based on the visual informa-tion.
This thesis proposes a series of models and methods to solve visual semantic rep-resentation and measurements. The contributions are not only on low level feature and representation, but also on high level model and measurements. The published papers cover all four elements of the research problem. It provides exploring report on the vi-sual semantic representation and measurements. The concrete contributions are listed as follows:
1. We propose the visual language model (VLM) to bridge the gap be-tween text analysis and the visual analysis. We believe that the local visual features follow certain grammar, which is similar the the words in text documents. By ana-lyzing the local features, we can estimate the semantic in an image. Since this model is similar to the language model in text analysis, lots of similar techniques can be ex-tended. Experimental results show that the model is effective and much faster than other complex models.
2. We propose the semantic preserving bag of words model to handle the semantic gap problem. We propose a novel measurement of the semantic gap, and try to find a best mapping space to translate the visual features to visual words that minimizing the semantic gap. In this way, we can better learn a dictionary with better discrim-ination. Experiments show that the optimal dictionary can significantly improve the performance of image annotation.
3. We propose probabilistic relevance component analysis method (pRCA) to help improve image similarity measurement. pRCA represents the side information between images in a probabilistic form rather than the binary form, to help improve distance learning. Experiments on Web image annotation show that the method is much better in accuracy and efficiency than other distance metric learning methods.
4. We propose a visual information based concept distance measurement, called Flickr distance. We believe correlated concepts have better chance to appear in the same image. Thus we can effectively measure the distance between concepts by the difference between their visual language models. Different from other text based con-cept distance measurements, Flickr distance adopts the visual information related to the concept. It can be effective for multimedia related tasks and is more consistent to human cognition.
5. We extend the traditional linear distance metric learning to the non-linear dis-tance function learning by proposing Bregman distance function learning. The tra-ditional Mahalanobis distance aims to learn a distance matrix, which is static for the whole sample space. Since the distribution of samples is quite different, it makes sense to consider local information of the sample distribution by adopting the non-learning distance function. Experiments show that the proposed Bregman distance can better handle distance learning problems in high dimensional space.
6. We extend the statistical distance measurement to dynamic distance measure-ment by proposing the QOSS subspace shifting method. We believe distance can be quite different in different metric space. In order to judge whether the two samples are similar or not, it is better to measure their distance in multiple spaces rather than simple space. The proposed method can automatically choose subspace for distance measure-ments. Experiments on Web image near duplicate detection show that our method can converge in less than 5 iterations and the detection precision can be significantly im-proved.
目前来看,视觉语义的表达和度量主要包含四个基本问题,即图像表达、图像相似度量、概念表达和概念相关度量。图像表达是指图像的特征以及特征的组织形式。图像特征可以生成有一定分辨能力的视觉单词、视觉短语。图像的表达形式也是多种多样,比如有忽略特征间关系的,有考虑在一幅图像中空间关系的或者共发频率的,等等。图像的距离度量是在特定图像表达之上通过机器学习的方法得到的。选择不同的图像特征和图像表达,图像的距离度量或许不一样。同样,概念表达是指概念的特征及其组织形式。概念的特征是以众多包含某个概念的图像集合上提取的视觉特征为基础建立的模型,它的通常形式是某个视觉单词或者短语出现的频率、共发频率、条件分布、或者转移概率。目前有很多概念建模的方法,比如二维隐马尔可夫模型、条件随机场模型等。本文的后一部分章节将详细讨论我们提出的视觉语言模型,保语义单词包模型,并和其他各种模型进行比较。概念距离度量是建立在特定的概念表达模型或者文本语义关联之上的概念之间的相关性度量。目前常用的度量方法包括词网距离、谷歌距离、以及我们提出的Flickr距离。
本文提出了解决视觉语义的表达和度量的一系列模型和方法,既有底层特征的创新,也有高层语义模型和距离度量方面的创新。发表的工作涉及了以上提及的四个方面挑战,为视觉语义的表达和度量相关研究提供了有意义的探索。具体来说本文的成果和创新之处包括以下几点：
1.本文提出了视觉语言模型,减小了视觉领域和文本领域的语义分析的鸿沟。我们认为图像的局部特征和文本中的单词一样是满足一定的语法顺序的。利用计算这些局部特征在空间位置上的条件分布来表达这种语法顺序,就可以近似的估测图像中的视觉语义。因为该模型和文本分析中的自然语言模型形式上类似,因此很多文本分析中的方法可以很容易在该模型中推广。实验结果表明该模型效果和很多复杂的模型相近,但运算速度远远超过其它模型,可以很好的应用在大规模数据上。
2.本文提出了保语义单词包模型来处理语义鸿沟问题。我们提出了一种语义鸿沟度量方法,并通过选择从视觉特征到视觉单词的映射空间来最小化语义鸿沟,从而使得我们产生的词典可以有更好的分辨能力。实验也证明了利用最小化语义鸿沟方法生成的词典在图像标注问题中效果明显优于其他方法。
3.本文提出了概率相关成分分析方法用来改进图像相似度量。概率相关成分分析将图像之间的边信息表达为概率的形式而不是传统的非0即1的二进制表达,提高了图像距离学习的精度。网络图像标注的应用显示出该方法比传统的距离学习方法更加高效和准确。
4.本文提出了基于视觉特征的概念相关性度量方法：Flickr距离。该距离可以用来度量两个概念的不相关度。我们认为相关的概念同时出现在同一幅图像中概率比较大。因此计算和两个概念分别相关的图像的视觉语言模型的差别,就可以有效地度量概念之间的不相关度。和其它基于文本的概念距离度量方法不同,Flickr距离应用了概念相关的图片信息,从视觉角度度量了概念的相关性。在多媒体相关的应用问题中可以显著地提高性能。和人工建立的词网距离比较,Flickr距离可以自动更新以覆盖更多的新概念,和传统的谷歌距离比较,Flickr距离利用了视觉信息,实验证明其更加符合人类的认知。
5.本文将传统的线性空间距离学习推广到了非线性空间距离,提出了Bregman距离函数学习方法。传统的Mahalanobis距离学习是需要学习一个距离矩阵。该距离度量在整个空间中是一致的。而样本在空间的分布疏密可能是有差别的。利用Bregma距离学习可能得到一个和样本相关的度量,考虑了局部分布的特性,因此可能更加准确。实验表明该方法可以比其他方法更好地处理高维空间的距离学习问题。
6.本文将传统的静态距离推广到了动态距离,提出了QOSS子空间选取方法。我们认为观测角度(度量空间)不同,对样本的距离度量会产生很大的影响。因此在判断两个样本是否相近的时候,在多个子空间中度量比在单个子空间度量要准确。我们提出了根据样本特性,自动选取多个子空间的策略对样本相似性进行度量。在网络图像近似重复检测中,我们发现经过不超过5轮迭代,检测精度可以显著提高。
With the development of Internet, there is proliferation of Web image resources. Lots of research problems come along with Web images, such as image annotation, image retrieval, search result clustering, near-duplicate detection, image tag recom-mendation, image indexing, image classification, object detection, etc. All these search topics have to deal with one intrinsic and fundamental problem, that is visual seman-tic representation and measurements. Thus this problem has become a hot research direction for both academy and industry.
Currently, visual semantic representation and measurements can be divided into four elements, including image representation, image similarity measurement, concept representation and concept correlation measurement. Image representation refers to im-age feature and the arrangement of features. The feature types could be various and so is the arrangements. Image similarity measurement is based on the image representa-tion, and could be generated by machine learning technology. Similarity measurement could be quite different by choosing different image features and the representations. Concept representation refers to the features of concept and their arrangement. The features of concept can be generated from a collection of images related to the concept. Currently there are lots of successful concept modeling methods, such as 2D hidden Markov model, conditional random fields, etc. Comparing to these complex models, we propose the visual language model, which is simple and effective. We also propose the semantic preserving bag of words model to help solve the semantic gap problem. The correlation between concepts is based on the concept models. As far as we know, there are several concept distance measurements, such as WordNet distance, google distance, etc. These distance are based on human labor or text information, while our proposed Flickr distance measures the concept distance based on the visual informa-tion.
This thesis proposes a series of models and methods to solve visual semantic rep-resentation and measurements. The contributions are not only on low level feature and representation, but also on high level model and measurements. The published papers cover all four elements of the research problem. It provides exploring report on the vi-sual semantic representation and measurements. The concrete contributions are listed as follows:
1. We propose the visual language model (VLM) to bridge the gap be-tween text analysis and the visual analysis. We believe that the local visual features follow certain grammar, which is similar the the words in text documents. By ana-lyzing the local features, we can estimate the semantic in an image. Since this model is similar to the language model in text analysis, lots of similar techniques can be ex-tended. Experimental results show that the model is effective and much faster than other complex models.
2. We propose the semantic preserving bag of words model to handle the semantic gap problem. We propose a novel measurement of the semantic gap, and try to find a best mapping space to translate the visual features to visual words that minimizing the semantic gap. In this way, we can better learn a dictionary with better discrim-ination. Experiments show that the optimal dictionary can significantly improve the performance of image annotation.
3. We propose probabilistic relevance component analysis method (pRCA) to help improve image similarity measurement. pRCA represents the side information between images in a probabilistic form rather than the binary form, to help improve distance learning. Experiments on Web image annotation show that the method is much better in accuracy and efficiency than other distance metric learning methods.
4. We propose a visual information based concept distance measurement, called Flickr distance. We believe correlated concepts have better chance to appear in the same image. Thus we can effectively measure the distance between concepts by the difference between their visual language models. Different from other text based con-cept distance measurements, Flickr distance adopts the visual information related to the concept. It can be effective for multimedia related tasks and is more consistent to human cognition.
5. We extend the traditional linear distance metric learning to the non-linear dis-tance function learning by proposing Bregman distance function learning. The tra-ditional Mahalanobis distance aims to learn a distance matrix, which is static for the whole sample space. Since the distribution of samples is quite different, it makes sense to consider local information of the sample distribution by adopting the non-learning distance function. Experiments show that the proposed Bregman distance can better handle distance learning problems in high dimensional space.
6. We extend the statistical distance measurement to dynamic distance measure-ment by proposing the QOSS subspace shifting method. We believe distance can be quite different in different metric space. In order to judge whether the two samples are similar or not, it is better to measure their distance in multiple spaces rather than simple space. The proposed method can automatically choose subspace for distance measure-ments. Experiments on Web image near duplicate detection show that our method can converge in less than 5 iterations and the detection precision can be significantly im-proved.
引文
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