基于马尔科夫稳态特性的图像检索系统设计与实现
发布时间：2019-04-11 17:07
【摘要】：随着计算机、多媒体以及Internet等技术的发展,尤其是搜索引擎的广泛应用,人们越来越多的接触到大量的图像数据。如何快速有效地从大规模图像数据库中检索出所需的图像已经成为目前信息检索领域非常重要也非常有挑战性的一个课题。基于内容的图像检索正是解决这一问题的比较智能且高效的方法。 基于内容的图像检索方法是根据图像中包含物体的类别进行分类的方法,其中图像特征提取以及分类检索是最关键的两步,对检索性能起到关键作用,也是近几年的热门研究课题。现有算法比如基于梯度直方图的图像特征提取(HOG),金字塔型的梯度直方图(PHOG)[4]或者SVM分类模型等,尽管已经取得了很大的成功,但是基于内容的图像检索依然是一个很有挑战性的任务,并且检索结果远不能让人满意。原因之一在于图像的描述符,即图像的特征提取不充分,另一个原因在于语义相关的图像在特征空间中,是内嵌在一个几何流形域中,而不是在线性的超平面中。 本课题研究从图像的特征提取及图像检索的快速算法两个角度出发,在图像特征提取阶段,主要提取的是图像的梯度信息,并通过马尔科夫稳态特征的分析,得到该图像基于马尔科夫稳态特性的梯度直方图特征。在图像分类检索阶段,主要使用了基于几何流形能量最小化的图像检索方法,将图像的检索看作一个在图像数据库中搜索一个最优图像能量环的问题。实验结果表明本文提出的图像检索框架是可行的,并且基于马尔科夫稳态特性的梯度直方图特征在图像特征表现上明显优于原始的HOG描述符。 本文的工作主要集中在以下几点： ●在图像提取阶段,对图像的梯度直方图特征进行扩展。根据马尔科夫链模型,特征化梯度直方图的空间共生情况,最终通过马尔科夫稳态特性,得到图像基于马尔科夫稳态特性的梯度直方图特征(GHMSF). GHMSF是对图像的梯度直方图特征更进一步的扩展,其中包含了直方图通道内部、直方图通道与通道之间的空间结构信息。 ●在图像分类检索阶段是基于几何流形域,同时将图像检索问题看作是图像数据库中搜索最优图像环的问题,避免了求解搜索特征空间到语义流形空间的映射关系。 ●禁忌搜索是一个组合优化问题,因此挑选最优候选解非常耗时。在本文研究中,在搜索过程中采用主动禁忌搜索方法来提高检索效率。
[Abstract]:With the development of computer, multimedia and Internet technology, especially the extensive application of search engine, more and more people come into contact with a large amount of image data. How to retrieve the required images from large-scale image databases quickly and effectively has become a very important and challenging topic in the field of information retrieval. Content-based image retrieval is an intelligent and efficient method to solve this problem. Content-based image retrieval is a classification method based on the classification of objects in the image. Image feature extraction and classification retrieval are the most important two steps, which play a key role in the retrieval performance. It is also a hot research topic in recent years. Existing algorithms such as gradient histogram-based image feature extraction (HOG), pyramid-type gradient histogram (PHOG) [4] or SVM classification model and so on, although it has achieved great success. However, content-based image retrieval is still a challenging task, and the retrieval results are far from satisfactory. One of the reasons lies in the inadequate feature extraction of the image descriptor, and the other is that the semantic-related image is embedded in a geometric flow field rather than in a linear hyperplane in the feature space, and the other reason is that the semantic-related image is embedded in a geometric flow field rather than in a linear hyperplane. In the stage of image feature extraction, the gradient information of the image is mainly extracted, and through the analysis of Markov steady-state features, this paper studies the feature extraction of image and the fast algorithm of image retrieval from two angles: image feature extraction and fast algorithm of image retrieval. The gradient histogram features of the image based on Markov steady-state characteristics are obtained. In the stage of image classification and retrieval, the image retrieval method based on geometric manifold energy minimization is mainly used, and the image retrieval is regarded as a problem of searching an optimal image energy loop in the image database. The experimental results show that the proposed image retrieval framework is feasible, and the gradient histogram features based on Markov steady-state characteristics are obviously superior to the original HOG descriptors in the performance of image features. The work of this paper mainly focuses on the following points: in the image extraction stage, the gradient histogram features of the image are extended. According to Markov chain model, the spatial symbiosis of gradient histogram is characterized. Finally, the gradient histogram feature (GHMSF). Of image based on Markov steady state is obtained through Markov steady state characteristic. GHMSF is a further extension of the gradient histogram feature of the image, which includes the spatial information between the histogram channel and the histogram channel. In the stage of image classification and retrieval, the problem of image retrieval is based on geometric flow domain. At the same time, the problem of image retrieval is regarded as the problem of searching the optimal image ring in the image database, which avoids the mapping from searching feature space to semantic manifold space. Tabu search is a combinatorial optimization problem, so it is time-consuming to select the optimal solution. In this paper, the active Tabu search method is used to improve the retrieval efficiency.
【学位授予单位】：复旦大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP391.41
[Abstract]:With the development of computer, multimedia and Internet technology, especially the extensive application of search engine, more and more people come into contact with a large amount of image data. How to retrieve the required images from large-scale image databases quickly and effectively has become a very important and challenging topic in the field of information retrieval. Content-based image retrieval is an intelligent and efficient method to solve this problem. Content-based image retrieval is a classification method based on the classification of objects in the image. Image feature extraction and classification retrieval are the most important two steps, which play a key role in the retrieval performance. It is also a hot research topic in recent years. Existing algorithms such as gradient histogram-based image feature extraction (HOG), pyramid-type gradient histogram (PHOG) [4] or SVM classification model and so on, although it has achieved great success. However, content-based image retrieval is still a challenging task, and the retrieval results are far from satisfactory. One of the reasons lies in the inadequate feature extraction of the image descriptor, and the other is that the semantic-related image is embedded in a geometric flow field rather than in a linear hyperplane in the feature space, and the other reason is that the semantic-related image is embedded in a geometric flow field rather than in a linear hyperplane. In the stage of image feature extraction, the gradient information of the image is mainly extracted, and through the analysis of Markov steady-state features, this paper studies the feature extraction of image and the fast algorithm of image retrieval from two angles: image feature extraction and fast algorithm of image retrieval. The gradient histogram features of the image based on Markov steady-state characteristics are obtained. In the stage of image classification and retrieval, the image retrieval method based on geometric manifold energy minimization is mainly used, and the image retrieval is regarded as a problem of searching an optimal image energy loop in the image database. The experimental results show that the proposed image retrieval framework is feasible, and the gradient histogram features based on Markov steady-state characteristics are obviously superior to the original HOG descriptors in the performance of image features. The work of this paper mainly focuses on the following points: in the image extraction stage, the gradient histogram features of the image are extended. According to Markov chain model, the spatial symbiosis of gradient histogram is characterized. Finally, the gradient histogram feature (GHMSF). Of image based on Markov steady state is obtained through Markov steady state characteristic. GHMSF is a further extension of the gradient histogram feature of the image, which includes the spatial information between the histogram channel and the histogram channel. In the stage of image classification and retrieval, the problem of image retrieval is based on geometric flow domain. At the same time, the problem of image retrieval is regarded as the problem of searching the optimal image ring in the image database, which avoids the mapping from searching feature space to semantic manifold space. Tabu search is a combinatorial optimization problem, so it is time-consuming to select the optimal solution. In this paper, the active Tabu search method is used to improve the retrieval efficiency.
【学位授予单位】：复旦大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP391.41
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