基于内容图像检索关键技术的研究
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摘要
伴随着计算机科学技术的高速发展,以及互联网络技术的迅速普及。与此同时,由于数字图像数量的激增,对其进行快速而有效的检索的要求愈加强烈。在这种背景下,基于内容的图像检索技术得到了广泛的发展。如何迅速而准确地从浩瀚的海量图像数据库中检索到所需的图像,成了近年来多媒体领域的研究热点问题。为了实现快速而准确地检索图像,利用图像视觉特征,如颜色、形状、纹理来检索图像,基于内容的图像检索的技术因此应运而生。本文针对其中的一些关键技术和理论方法作了如下四个方面的工作:
(1)提出了一种结合人的视觉感知的HSV彩色空间模型的图像检索方法。通过对HSV色彩空间的分析,通过对相似色区域的划分提出了从视觉上更合理的不等间隔的量化方法,使其更符合人的主观视觉模型,本文对HSV色彩空间非等间隔量化的研究,并借鉴认知心理学的研究成果,提出了与人的视觉感知相一致HSV的量化模型,在此基础上,给出了基于HSV模型特征的图像检索算法,有效地降低了算法复杂度,提高了检索效率。
(2)提出一种径向基函数神经网络(RBFNN)建模方法。利用该方法将图像数据库中的每一幅图像映射到语义空间中;利用RBFNN逼近从低层特征空间到语义空间的最优化映射。检索过程中的计算复杂性随着语义空间的维数的减少而减少,从而避免了维数灾难。将相关反馈技术和径向神经网络技术应用于图像检索中,取得了较好的效果。
(3)提出一种支撑向量机(SVM)图像检索模型。相关反馈技术是图像检索过程中的一种交互式检索技术,允许用户对检索结果进行标注和评价,通过学习用户反馈的相关信息,来改进检索结果。本文构造SVM的分类器,提出了基于SVM的交互式图像检索算法。实验结果表明,该方法在合适的样本下,可以有效地改进检索结果,使之更为符合用户的需求。
(4)对语义建模技术进行了有益的尝试,提出了一种基于图像特征的语义检索,它弥补了基于视觉特征的不足,体现了以人为本的理念。语义建模技术来弥补这方面的不足,语义标注方法根据是否将关键词作为进行判别分为监督语义标注,通过对无监督语义标注的研究,提出了一对多的语义标注策略,以有利于语义空间的图像检索,该方法是基于用户的隐语义特征。从人的认知角度来理解图像语义信息,最大程度地弥合图像低层特征和高层语义之间的语义鸿沟。
With Digital Technology the rapid development of digital cameras, video cameras and other digital equipment, flying into the homes of ordinary people. A variety of multimedia data such as images, video and other rapidly expanding gradually become the information in the mainstream, and people's lives have an important impact. As the image data rendering geometric progression of growth, the need for images has become in recent years, image retrieval on the main hot spot. Meanwhile, Image retrieval techniques, but also caused the academic and industry attention.
Study of visual perception of the retrieval and modeling, and analysis of their learning mechanism, and explore in line with the human perception of visual media interaction, has become the image retrieval as an important area. From nineties began to people from the visual aspects of the image features in-depth study. Colors relative to the geometric shape, the right translation, rotation, scaling transformation with invariance demonstrated considerable robustness. We passed on several common color modeling principles for discussion and analysis, and drawing on cognitive psychology research, presented with the human visual perception consistent with HSV quantitative model, on this basis is given based on HSV Model Image Retrieval algorithm, effectively reduces the complexity of the algorithm to improve the retrieval efficiency.
In the image retrieval process, the introduction of machine learning methods can make use of the existing machine learning field theory of the learning process research and analysis. The data-based machine learning of modern intelligence technology in the important aspects. Research from the observed sample off to find the laws, the use of these laws for the future of data or not observed data to predict. Including pattern recognition, neural network, etc., the existing machine learning methods based on shared premises is one of statistics, the traditional statistics is the study of the number of samples tends to infinity when the incremental theory of theory, the existing learning methods are mostly based on the this assumption.
Based on image feature extraction method can greatly promote the content-based image retrieval research, it is based on the image's own content. However, due to the image low-level features and high-level semantics between the semantic gap, can not use the image low-level feature accurately convey the image of the high-level semantics. In order to effectively address the outstanding issues, on the one hand the need in image processing research in the field of effective feature representation method, on the other hand is an attempt to capture and build low-level features and high-level semantic link between, is how to narrow the semantic gap problem.
Support vector machines in recent years in the statistical learning theory developed on the basis of machine learning methods, this paper, the SVM method of study will SVM for image retrieval, interactive learning process, the user feedback and relevant information as the two classification of training samples, by learning to be two classifiers, and then the entire image retrieval is proposed based on support vector machine SVM interactive image retrieval algorithm. Experimental results show that, SVM method under ideal conditions to achieve a better than other machine learning methods of search results. Kernel function selection problem is usually in the SVM method is a more difficult problem, the optimal kernel function selection has no theoretical methodological guidance, the same, covering the problem is also a NP problem, not solving the optimal solution, it is still not certain the theory to guide. The use of nuclear function, so that the sample is mapped to a high-dimensional space, and then construct the optimal separating surface. Therefore, kernel function selection directly affects the classifier's generalization ability.
Kernel function selection, including nuclear functions and parameters to determine the two aspects. Through the experimental comparison concludes that Gaussian kernel function in the image retrieval learning the best, so we choose Gaussian kernel function as kernel function, and then Gaussian kernel function parameters by empirical means to select and defined. Experiments show that illustrates the SVM in the first training sample cases, still be able to maintain a good learning ability and generalization ability.
Relevance feedback technique is an interactive search technology, allowing users to retrieve the results marked by learning user feedback and relevant information to improve search results. In this paper, image retrieval in this interactive learning process, and draw on the relevant feedback technique is proposed based on radial basis function to the neural network learning algorithm for interactive image retrieval methods, the use of multiple neurons covered by the geometric area of the combination of sample data to describe the effective representation of the relevant images in the feature space distribution. The experimental results show that this method can effectively improve the search results to make it more in line with the needs of users. As the neural network and the human brain has a similar mechanism, therefore, for large-capacity parallel image retrieval with with a clear advantage, and achieved good results.
In order to overcome the semantic gap of the impact of this paper, semantic-based technology research in this area. Images contain rich semantics, in the semantic image retrieval research occupies an important position. Semantic description, feature extraction, recognition is semantic retrieval of the most central issue. Presented based on image characteristics of semantic retrieval, which made up based on visual features of the shortage reflects the people-centered concept. Based on the analysis semantic retrieval context, analysis monitoring semantic annotation and unsupervised semantic annotation. Are given based on semantic space, the image method is based on the user's implicit semantic features. And to achieve a semantic image retrieval method, which in fact based on the semantic space for retrieval of a special case, will the user's implicit semantic feature explicitly for explicit semantics. The image contains rich semantics, semantics as an important high-level semantic content, semantic image retrieval research occupies an important position. Semantic description, feature extraction, recognition is the emotional semantic retrieval of the most central issues. This in-depth study of these issues was proposed based on a new image content description framework, and implemented an image recognition prototype system. The results show that the prototype has achieved good results. Image Retrieval as a challenging research and a project of application, have broad application prospects. At the same time as time goes on, image retrieval from the laboratory to the real practical use would be just around the corner.
(1)提出了一种结合人的视觉感知的HSV彩色空间模型的图像检索方法。通过对HSV色彩空间的分析,通过对相似色区域的划分提出了从视觉上更合理的不等间隔的量化方法,使其更符合人的主观视觉模型,本文对HSV色彩空间非等间隔量化的研究,并借鉴认知心理学的研究成果,提出了与人的视觉感知相一致HSV的量化模型,在此基础上,给出了基于HSV模型特征的图像检索算法,有效地降低了算法复杂度,提高了检索效率。
(2)提出一种径向基函数神经网络(RBFNN)建模方法。利用该方法将图像数据库中的每一幅图像映射到语义空间中;利用RBFNN逼近从低层特征空间到语义空间的最优化映射。检索过程中的计算复杂性随着语义空间的维数的减少而减少,从而避免了维数灾难。将相关反馈技术和径向神经网络技术应用于图像检索中,取得了较好的效果。
(3)提出一种支撑向量机(SVM)图像检索模型。相关反馈技术是图像检索过程中的一种交互式检索技术,允许用户对检索结果进行标注和评价,通过学习用户反馈的相关信息,来改进检索结果。本文构造SVM的分类器,提出了基于SVM的交互式图像检索算法。实验结果表明,该方法在合适的样本下,可以有效地改进检索结果,使之更为符合用户的需求。
(4)对语义建模技术进行了有益的尝试,提出了一种基于图像特征的语义检索,它弥补了基于视觉特征的不足,体现了以人为本的理念。语义建模技术来弥补这方面的不足,语义标注方法根据是否将关键词作为进行判别分为监督语义标注,通过对无监督语义标注的研究,提出了一对多的语义标注策略,以有利于语义空间的图像检索,该方法是基于用户的隐语义特征。从人的认知角度来理解图像语义信息,最大程度地弥合图像低层特征和高层语义之间的语义鸿沟。
With Digital Technology the rapid development of digital cameras, video cameras and other digital equipment, flying into the homes of ordinary people. A variety of multimedia data such as images, video and other rapidly expanding gradually become the information in the mainstream, and people's lives have an important impact. As the image data rendering geometric progression of growth, the need for images has become in recent years, image retrieval on the main hot spot. Meanwhile, Image retrieval techniques, but also caused the academic and industry attention.
Study of visual perception of the retrieval and modeling, and analysis of their learning mechanism, and explore in line with the human perception of visual media interaction, has become the image retrieval as an important area. From nineties began to people from the visual aspects of the image features in-depth study. Colors relative to the geometric shape, the right translation, rotation, scaling transformation with invariance demonstrated considerable robustness. We passed on several common color modeling principles for discussion and analysis, and drawing on cognitive psychology research, presented with the human visual perception consistent with HSV quantitative model, on this basis is given based on HSV Model Image Retrieval algorithm, effectively reduces the complexity of the algorithm to improve the retrieval efficiency.
In the image retrieval process, the introduction of machine learning methods can make use of the existing machine learning field theory of the learning process research and analysis. The data-based machine learning of modern intelligence technology in the important aspects. Research from the observed sample off to find the laws, the use of these laws for the future of data or not observed data to predict. Including pattern recognition, neural network, etc., the existing machine learning methods based on shared premises is one of statistics, the traditional statistics is the study of the number of samples tends to infinity when the incremental theory of theory, the existing learning methods are mostly based on the this assumption.
Based on image feature extraction method can greatly promote the content-based image retrieval research, it is based on the image's own content. However, due to the image low-level features and high-level semantics between the semantic gap, can not use the image low-level feature accurately convey the image of the high-level semantics. In order to effectively address the outstanding issues, on the one hand the need in image processing research in the field of effective feature representation method, on the other hand is an attempt to capture and build low-level features and high-level semantic link between, is how to narrow the semantic gap problem.
Support vector machines in recent years in the statistical learning theory developed on the basis of machine learning methods, this paper, the SVM method of study will SVM for image retrieval, interactive learning process, the user feedback and relevant information as the two classification of training samples, by learning to be two classifiers, and then the entire image retrieval is proposed based on support vector machine SVM interactive image retrieval algorithm. Experimental results show that, SVM method under ideal conditions to achieve a better than other machine learning methods of search results. Kernel function selection problem is usually in the SVM method is a more difficult problem, the optimal kernel function selection has no theoretical methodological guidance, the same, covering the problem is also a NP problem, not solving the optimal solution, it is still not certain the theory to guide. The use of nuclear function, so that the sample is mapped to a high-dimensional space, and then construct the optimal separating surface. Therefore, kernel function selection directly affects the classifier's generalization ability.
Kernel function selection, including nuclear functions and parameters to determine the two aspects. Through the experimental comparison concludes that Gaussian kernel function in the image retrieval learning the best, so we choose Gaussian kernel function as kernel function, and then Gaussian kernel function parameters by empirical means to select and defined. Experiments show that illustrates the SVM in the first training sample cases, still be able to maintain a good learning ability and generalization ability.
Relevance feedback technique is an interactive search technology, allowing users to retrieve the results marked by learning user feedback and relevant information to improve search results. In this paper, image retrieval in this interactive learning process, and draw on the relevant feedback technique is proposed based on radial basis function to the neural network learning algorithm for interactive image retrieval methods, the use of multiple neurons covered by the geometric area of the combination of sample data to describe the effective representation of the relevant images in the feature space distribution. The experimental results show that this method can effectively improve the search results to make it more in line with the needs of users. As the neural network and the human brain has a similar mechanism, therefore, for large-capacity parallel image retrieval with with a clear advantage, and achieved good results.
In order to overcome the semantic gap of the impact of this paper, semantic-based technology research in this area. Images contain rich semantics, in the semantic image retrieval research occupies an important position. Semantic description, feature extraction, recognition is semantic retrieval of the most central issue. Presented based on image characteristics of semantic retrieval, which made up based on visual features of the shortage reflects the people-centered concept. Based on the analysis semantic retrieval context, analysis monitoring semantic annotation and unsupervised semantic annotation. Are given based on semantic space, the image method is based on the user's implicit semantic features. And to achieve a semantic image retrieval method, which in fact based on the semantic space for retrieval of a special case, will the user's implicit semantic feature explicitly for explicit semantics. The image contains rich semantics, semantics as an important high-level semantic content, semantic image retrieval research occupies an important position. Semantic description, feature extraction, recognition is the emotional semantic retrieval of the most central issues. This in-depth study of these issues was proposed based on a new image content description framework, and implemented an image recognition prototype system. The results show that the prototype has achieved good results. Image Retrieval as a challenging research and a project of application, have broad application prospects. At the same time as time goes on, image retrieval from the laboratory to the real practical use would be just around the corner.
引文
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