基于词袋模型的图像分类技术研究
发布时间：2018-08-27 11:02
【摘要】：图像分类是数字图像分析与理解的主要研究方向,因而受到人们广泛的关注,如何对大量的图像进行快速、准确的分类是当前研究中的一个热点问题。 本文将词袋模型应用于无监督图像分类,并针对传统词袋模型中所涉及方法(如特征提取、聚类方法等)的不足,做出了如下的改进： 首先,采用加速鲁棒特征变换(Speeded Up Robust Features, SURF)提取特征(图像词语)。本文将增加预处理环节,即通过特征兴趣区域(Region of interesting Features, ROIF)与图像前景对象范围定位方法对特征提取范围进行规定。实验表明,预处理环节可有效减少特征中弱特征与无关特征数量。 其次,本文采用精确欧拉位置敏感哈希(Exact Euclidean Locality Sensitive Hashing, E2LSH)对图像词语聚类以获得关键词(聚类中心)。为了减小E2LSH的随机性,将多次聚类并使用基于最短特征无向图的组合汇集技术获得最终的聚类分布。实验表明,由E2LSH得到的关键词具有更强的代表性。 再次,利用吉布斯抽样计算隐狄利克雷分布模型(Latent Dirichlet Allocation, LDA)得到类别分布转移矩阵。并使用最大转移概率与转移向量相似度结合的组合方法阅读转移矩阵获得分类结果。实验表明,组合式阅读能更好的发现转移矩阵中隐含的类别信息。 最后,针对传统分类方法对同一类内图像间关系的忽视,利用双向匹配(Bidirectional Matching, BM),随机抽样一致性(Random Sample Consensus, RANSAC)和感知哈希(Perceptual Hash, pHash)进行同一类内图像间关系的寻找(特征匹配)。实验表明,通过上述方法可准确获得同一类内图像间的关系,实现类内图像的细化分类。
[Abstract]:Image classification is the main research direction of digital image analysis and understanding, so people pay more attention to it. How to classify a large number of images quickly and accurately is a hot issue in current research. This paper applies the word bag model to unsupervised image classification, and aiming at the shortcomings of the traditional word bag model (such as feature extraction, clustering method, etc.), the following improvements are made: first, An accelerated robust feature transform (Speeded Up Robust Features, SURF) is used to extract features (image words). In this paper, preprocessing is added, that is, the range of feature extraction is defined by the region of interest (Region of interesting Features, ROIF) and the image foreground object localization method. Experiments show that preprocessing can effectively reduce the number of weak and independent features. Secondly, the accurate Euler position sensitive (Exact Euclidean Locality Sensitive Hashing, E2LSH (Euler Hash (Exact Euclidean Locality Sensitive Hashing, E2LSH) is used to cluster the image words to obtain the key words (clustering center). In order to reduce the randomness of E2LSH, the final clustering distribution is obtained by using the combination aggregation technique based on the shortest feature undirected graph. Experiments show that the keywords obtained from E2LSH are more representative. Thirdly, the class distribution transfer matrix is obtained by using Gibbs sampling to calculate the hidden Dirichlet distribution model (Latent Dirichlet Allocation, LDA). A combination of maximum transition probability and transfer vector similarity is used to read the transition matrix to obtain the classification results. The experimental results show that the combined reading can better find the category information implied in the transfer matrix. Finally, aiming at the neglect of the relationship between images in the same class by the traditional classification methods, we use two-way matching (Bidirectional Matching, BM), random sampling consistent (Random Sample Consensus, RANSAC) and perceptual hash (Perceptual Hash, pHash) to find the relationship (feature matching) between images within the same class. The experimental results show that the relationship between the images in the same class can be accurately obtained by the above methods, and the thinning and classification of the images in the same class can be realized.
【学位授予单位】：安徽大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP391.41
[Abstract]:Image classification is the main research direction of digital image analysis and understanding, so people pay more attention to it. How to classify a large number of images quickly and accurately is a hot issue in current research. This paper applies the word bag model to unsupervised image classification, and aiming at the shortcomings of the traditional word bag model (such as feature extraction, clustering method, etc.), the following improvements are made: first, An accelerated robust feature transform (Speeded Up Robust Features, SURF) is used to extract features (image words). In this paper, preprocessing is added, that is, the range of feature extraction is defined by the region of interest (Region of interesting Features, ROIF) and the image foreground object localization method. Experiments show that preprocessing can effectively reduce the number of weak and independent features. Secondly, the accurate Euler position sensitive (Exact Euclidean Locality Sensitive Hashing, E2LSH (Euler Hash (Exact Euclidean Locality Sensitive Hashing, E2LSH) is used to cluster the image words to obtain the key words (clustering center). In order to reduce the randomness of E2LSH, the final clustering distribution is obtained by using the combination aggregation technique based on the shortest feature undirected graph. Experiments show that the keywords obtained from E2LSH are more representative. Thirdly, the class distribution transfer matrix is obtained by using Gibbs sampling to calculate the hidden Dirichlet distribution model (Latent Dirichlet Allocation, LDA). A combination of maximum transition probability and transfer vector similarity is used to read the transition matrix to obtain the classification results. The experimental results show that the combined reading can better find the category information implied in the transfer matrix. Finally, aiming at the neglect of the relationship between images in the same class by the traditional classification methods, we use two-way matching (Bidirectional Matching, BM), random sampling consistent (Random Sample Consensus, RANSAC) and perceptual hash (Perceptual Hash, pHash) to find the relationship (feature matching) between images within the same class. The experimental results show that the relationship between the images in the same class can be accurately obtained by the above methods, and the thinning and classification of the images in the same class can be realized.
【学位授予单位】：安徽大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP391.41
【参考文献】
相关期刊论文 前7条
1 茹朝阳;;基于SIFT特征的遥感影像自动配准[J];城市勘测;2009年06期
2 赵永威;李弼程;彭天强;高毫林;;一种基于随机化视觉词典组和查询扩展的目标检索方法[J];电子与信息学报;2012年05期
3 骞森;朱剑英;;基于改进的SIFT特征的图像双向匹配算法[J];机械科学与技术;2007年09期
4 严华;;一种改进的k-means算法[J];计算机与现代化;2009年01期
5 石晶;范猛;李万龙;;基于LDA模型的主题分析[J];自动化学报;2009年12期
6 何周灿;王庆;杨恒;;一种面向快速图像匹配的扩展LSH算法[J];四川大学学报(自然科学版);2010年02期
7 刘守群;朱明;郑p，
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