基于多特征的图像检索研究
发布时间：2019-03-23 20:26
【摘要】：图像数据作为互联网数据中重要的组成部分,随着互联网信息时代的快速发展以及拍照智能手机的大范围普及,在以惊人的速度不断地积累。相比文本数据,图像数据的优势在于提供了更加丰富和直观的内容资源。那么如何在含有大量图像的数据库中实现图像的有效组织和管理,以便人们快速检索和访问所需的图像,这已经成为信息时代越来越重要且具有挑战性的研究问题。最早发展和建立起来的图像检索系统大多都是基于文本检索方法的,其检索表现很大程度上依赖于人工标注的关键词信息,而人工标注的文本信息既带有主观性又无法将图像中包含的丰富信息完全表达出来。相比于基于文本的图像检索方法,传统的基于内容的图像检索方法避免了人工标注图像,但是其存在鲁棒性不强导致检索精度不高,或者检索效率太低等问题,都无法完全满足实际应用的需求。近十年来,在国内外的高校、科研机构的努力下,局部特征(如SIFT)和视觉词袋模型(bag-of-visual-words,BoVW)的提出和应用,大大推动了基于内容的图像检索向更高的层次发展。本文主要研究基于多特征的图像检索,提出了一种新的图像检索算法。该图像检索算法的核心是联合图像语义属性特征的二维倒排索引,能使那些与查询图像有大量相似局部特征且语义相似的候选图像在检索结果中排序更靠前,使得检索结果更符合用户需求。论文介绍了基于内容的图像检索所涉及到的相关理论,重点分析了传统的倒排索引和图像语义属性特征的提取,在此基础上引出了本文研究的主要内容:二维倒排索引的构建和二维倒排索引的更新。在二维倒排索引的构建阶段,首先利用高斯差分(DoG)检测出图像的尺度不变关键点(Keypoint),基于尺度不变关键点提取SIFT特征和CN(Color Names)颜色特征,然后采用独立数据集上训练的SIFT视觉词典和CN视觉词典对两种局部特征进行量化,随后图像的每个关键点都表示为一个视觉单词对,进而构建出二维倒排索引。二维倒排索引相当于在索引层面上融合了两种视觉特征信息,能减少关键点的匹配错误。在二维倒排索引的更新阶段,本文先将图像语义属性特征转变成概率向量,用概率向量的总方差距离(Total Variance Distance,TVD)衡量图像之间的语义相似度。然后遍历已构建的二维倒排索引,将与图像的内容语义很相似的若干数据库图像插入该图像所在的倒排索引项中,如果待插入的图像已经存在于当前倒排列表中就不执行插入操作。通过这种索引更新方式能在二维倒排索引中联合图像语义属性特征,使得图像检索精度在一定程度上得到提高。本文分别在Ukbench和Holidays图像数据集上对提出的检索算法进行了实验验证,实验结果表明本文提出的基于多特征的图像检索算法能够获得较好的检索表现,在近似重复图像检索中具有一定的应用价值。
[Abstract]:Image data as an important part of Internet data, with the rapid development of the Internet information age and the widespread popularity of photo-taking smartphones, it is accumulating at an astonishing speed. Compared with text data, the advantage of image data is that it provides more abundant and intuitive content resources. So how to organize and manage images effectively in the database containing a large number of images so that people can quickly retrieve and access the required images has become a more and more important and challenging research issue in the information age. Most of the earliest developed and established image retrieval systems are based on text retrieval methods, and their retrieval performance depends to a large extent on manually labeled keyword information. The text information of manual annotation is not only subjective but also unable to express the rich information contained in the image. Compared with the text-based image retrieval method, the traditional content-based image retrieval method avoids manual labeling of images, but its robustness is not strong enough to lead to low retrieval accuracy or low retrieval efficiency, and so on. Can not fully meet the needs of practical applications. In the past decade, with the efforts of domestic and foreign universities and scientific research institutions, local features (such as SIFT) and visual word bag model (bag-of-visual-words,BoVW) have been put forward and applied. It greatly promotes the development of content-based image retrieval to a higher level. In this paper, multi-feature-based image retrieval is studied, and a new image retrieval algorithm is proposed. The core of the image retrieval algorithm is the two-dimensional inverted index which combines the semantic attribute features of the image, which can make the candidate images which have a lot of similar local features and similar semantics of the query image rank further in the retrieval results. So that the retrieval results are more in line with the needs of the user. This paper introduces the related theories involved in content-based image retrieval, and focuses on the analysis of the traditional inverted index and the extraction of image semantic attribute features. On this basis, the main contents of this paper are introduced: the construction of two-dimensional inverted index and the updating of two-dimensional inverted index. In the construction stage of two-dimensional inverted index, firstly, Gao Si differential (DoG) is used to detect the scale-invariant key points of the image. (Keypoint), extracts the SIFT features and CN (Color Names) color features based on the scale-invariant keys. Then the SIFT visual dictionary and the CN visual dictionary trained on the independent dataset are used to quantify the two local features. Then each key point of the image is represented as a visual word pair, and then a two-dimensional inverted index is constructed. The two-dimensional inverted index is equivalent to the fusion of two kinds of visual feature information at the index level, which can reduce the matching errors of key points. In the updating stage of two-dimensional inverted index, the semantic attribute feature of image is transformed into probability vector firstly, and the semantic similarity between images is measured by the total variance distance of probability vector (Total Variance Distance,TVD). You then traverse the built two-dimensional inverted index and insert a number of database images that are similar to the content semantics of the image into the inverted index item in which the image is located. Inserts are not performed if the image to be inserted already exists in the current inverted list. Through this index updating method, we can combine semantic attribute features in two-dimensional inverted index, and improve the image retrieval accuracy to a certain extent. The experimental results show that the proposed image retrieval algorithm based on multi-features can achieve better retrieval performance in the Ukbench and Holidays image datasets, and the experimental results show that the proposed image retrieval algorithm based on multi-feature can achieve better retrieval performance. It has certain application value in approximate repeated image retrieval.
【学位授予单位】：西南大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
本文编号：2446198
[Abstract]:Image data as an important part of Internet data, with the rapid development of the Internet information age and the widespread popularity of photo-taking smartphones, it is accumulating at an astonishing speed. Compared with text data, the advantage of image data is that it provides more abundant and intuitive content resources. So how to organize and manage images effectively in the database containing a large number of images so that people can quickly retrieve and access the required images has become a more and more important and challenging research issue in the information age. Most of the earliest developed and established image retrieval systems are based on text retrieval methods, and their retrieval performance depends to a large extent on manually labeled keyword information. The text information of manual annotation is not only subjective but also unable to express the rich information contained in the image. Compared with the text-based image retrieval method, the traditional content-based image retrieval method avoids manual labeling of images, but its robustness is not strong enough to lead to low retrieval accuracy or low retrieval efficiency, and so on. Can not fully meet the needs of practical applications. In the past decade, with the efforts of domestic and foreign universities and scientific research institutions, local features (such as SIFT) and visual word bag model (bag-of-visual-words,BoVW) have been put forward and applied. It greatly promotes the development of content-based image retrieval to a higher level. In this paper, multi-feature-based image retrieval is studied, and a new image retrieval algorithm is proposed. The core of the image retrieval algorithm is the two-dimensional inverted index which combines the semantic attribute features of the image, which can make the candidate images which have a lot of similar local features and similar semantics of the query image rank further in the retrieval results. So that the retrieval results are more in line with the needs of the user. This paper introduces the related theories involved in content-based image retrieval, and focuses on the analysis of the traditional inverted index and the extraction of image semantic attribute features. On this basis, the main contents of this paper are introduced: the construction of two-dimensional inverted index and the updating of two-dimensional inverted index. In the construction stage of two-dimensional inverted index, firstly, Gao Si differential (DoG) is used to detect the scale-invariant key points of the image. (Keypoint), extracts the SIFT features and CN (Color Names) color features based on the scale-invariant keys. Then the SIFT visual dictionary and the CN visual dictionary trained on the independent dataset are used to quantify the two local features. Then each key point of the image is represented as a visual word pair, and then a two-dimensional inverted index is constructed. The two-dimensional inverted index is equivalent to the fusion of two kinds of visual feature information at the index level, which can reduce the matching errors of key points. In the updating stage of two-dimensional inverted index, the semantic attribute feature of image is transformed into probability vector firstly, and the semantic similarity between images is measured by the total variance distance of probability vector (Total Variance Distance,TVD). You then traverse the built two-dimensional inverted index and insert a number of database images that are similar to the content semantics of the image into the inverted index item in which the image is located. Inserts are not performed if the image to be inserted already exists in the current inverted list. Through this index updating method, we can combine semantic attribute features in two-dimensional inverted index, and improve the image retrieval accuracy to a certain extent. The experimental results show that the proposed image retrieval algorithm based on multi-features can achieve better retrieval performance in the Ukbench and Holidays image datasets, and the experimental results show that the proposed image retrieval algorithm based on multi-feature can achieve better retrieval performance. It has certain application value in approximate repeated image retrieval.
【学位授予单位】：西南大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41
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