网络图像检索关键技术研究

发布时间：2018-02-28 22:20

本文关键词： 图像处理网络图像检索特征提取显著区域稳定兴趣点优化反馈　出处：《西安电子科技大学》2014年博士论文　论文类型：学位论文

【摘要】：网络图像检索技术是信息检索的一个重要内容,也是当前图像处理和计算机视觉领域中的一个研究热点。该技术通过提取和分析网络图像的视觉特征,为用户提供相关的网络图像检索服务。其主要目的是克服基于关键词的检索方式的约束,有效帮助用户在海量信息中更快、更准地搜索出所需信息,实现“所见即所搜”的功能,它已在网络图像搜索引擎、商标检索、网络数字博物馆以及电子商务等领域得到广泛应用。目前网络图像检索仍存在着许多亟待解决的问题。如网络图像采用多种压缩格式来存储数据,容易导致图像细节信息的缺失,使得图像图像特征提取准确度降低;不同格式的网络图像构成的网络图像数据库规模巨大,且具有极高的复杂性和多样性,使得网络图像检索实时性降低。论文全面分析了基于视觉特征的图像检索方法,重点研究了图像局部特征提取算法以及相关反馈算法,针对网络图像检索中各个关键环节,提出了基于稳定兴趣点空域划分、基于显著区域不变特征和基于生态选择粒子群优化反馈的图像检索算法,并通过大量实验对提出算法进行了验证。最后,提出一种新的智能网络图像检索系统,并将论文提出的特征提取算法和优化反馈方法引入其中。论文的主要工作及贡献如下:1.提出了一种基于稳定兴趣点空域划分的图像检索算法(SIPRD)。针对传统的兴趣点检测器在检测网络图像时常会出现点位置偏差或误检测问题,对基于灰度的兴趣点检测算法性能及优缺点进行了分析,引入了一种基于优化梯度滤波(ODF)的兴趣点检测器来检测尺度归一化图像的稳定兴趣点,以降低不稳定兴趣点的干扰;利用稳定兴趣点高信息含量特性及其空间分布规律,对图像进行环形和凸包区域划分,同时使用凸包和环形颜色直方图的加权矢量来描述网络图像特征。实验表明,该算法能有效避免不稳定兴趣点带来的干扰,使网络图像局部区域特征的描述更为准确,检索的准确度大幅提高。2.提出了一种基于尺度不变特性的兴趣点检测算法(IPDSH)。针对尺度变换和仿射变换会造成网络图像中兴趣点丢失或出现伪兴趣点的问题,对图像进行不同尺度因子的卷积,以得到图像的多尺度空间;通过比较多尺度空间图像中每个像素同其相邻尺度和位置的像素的灰度,得到多尺度空间的极值点;根据每个尺度空间极值点邻域内的梯度变化值的大小以及给定的阈值,保留邻域内的梯度变化值较大的极值点,并将该极值点看做尺度空间中稳定的兴趣点。实验表明,该算法检索速度快,对图像旋转、平移具有鲁棒性,能有效提高相似图像尺度兴趣点的一致性,从而提高基于兴趣点的网络图像检索算法准确度。3.提出了一种基于显著区域不变特征的图像检索算法(SRIF)。针对传统基于兴趣点的区域划分方法易受图像中游离兴趣点干扰的问题,根据图像中IPDSH兴趣点的分布密度,采用遍历的方法来寻找显著区域,并确定尺度空间的显著兴趣点;使用显著区域内伪泽尼克矩来描述图像的不变特征,以提高特征描述的准确度。实验表明,该算法更加符合人眼视觉原理,能准确提取图像的显著区域,同时增加显著点的可靠性,提取的特征具有尺度不变性,不易受噪声攻击干扰,能有效提高网络图像检索的查准率和查全率。4.提出了一种基于生态选择粒子群优化反馈(r/KPSO-RF)的图像检索方法。针对传统基于相关反馈的图像检索算法在调整参数尺度时缺乏灵活的自适应调整空间的问题,系统研究了群体智能算法r/KPSO,并将其引入相关反馈过程;将待查询图像看做粒子来进行初始化,利用r/KPSO全局寻优、快速收敛的特点,在理想监督下指导粒子运动方向快速向最优解集靠拢;根据最优结果对特征权值参数进行自适应的调整,从而改善反馈性能。实验表明,在反馈训练样本少、实时性要求高、训练样本不对称以及存在大量的未标记样本时,该算法能准确理解用户反馈的真实意图,有效解决了优化目标细节的随机性,从而改善网络图像检索性能。5.提出了一种新的网络图像智能检索系统(WIIRS)。针对面向网络的图像检索系统进行深入研究,分析了系统中各个模块的功能,提出了一种新的由抓取模块、管理模块、检测模块三部分组成网络图像检索系统。该系统通过抓取模块来实现网络图像的收集和编号,同时建立图像数据库;检测模块中引入本文特征提取和优化反馈算法来进行图像特征描述,同时建立图像特征库;管理模块负责系统的稳定运行和人机交互的有效实现。最后,在WIIRS系统基础上,引入了色情图像特征提取算法,提出一种网络色情图像过滤系统(PIRF),以改善传统基于肤色模型的色情图像检测算法在检测色彩失真和亮度偏暗的色情图像时存在较高误检率的问题。实验表明,WIIRS系统可以有效实现网络图像检索功能,PIRF系统能有效地检测和过滤互联网中色情图像,具有较高的实用价值。
[Abstract]:Network image retrieval technology is an important content of information retrieval, it is also a research hotspot of current image processing and computer vision. The visual feature extraction and analysis of network image retrieval service, provide network image correlation for users. Its main purpose is to overcome the keyword based information retrieval constraints effectively to help users in the mass of information faster and more accurately search out the required information, the realization of "what you see is the search function, it has been in the network search engine, image retrieval has been widely used in network trademark, digital museum and e-commerce and other fields. The current network image retrieval there are still many problems to be solved such as compressed format to store data using a variety of network image, easy to cause the lack of details of the image, the image feature extraction reduces the accuracy of different formats; The network image forms a network image database is huge, and has a very high complexity and diversity, which reduces the real-time network image retrieval. This paper analyses the method of image retrieval based on visual features, focusing on the image local feature extraction algorithm and relevance feedback algorithm for image retrieval in network, each key link, put forward the stable points of interest based on the division of airspace, significant regional invariant features and ecological selection of particle swarm optimization of feedback image retrieval algorithm based on based on, and through a lot of experiments on the proposed algorithm is verified. Finally, this paper proposes a new intelligent network image retrieval system, and the feature extraction algorithm and optimization of the proposed feedback method is introduced. The main contributions of this dissertation are as follows: 1. propose an image retrieval algorithm based on stable interest point airspace division (SIPRD). In view of the traditional interest point detector in the detection of network image often appear position deviation or error detection problem of gray interest point detection algorithms and their advantages and disadvantages are analyzed based on the introduction of a gradient optimization filter (ODF) based on the interest point detector to detect scale normalized image stable points of interest, to to reduce the disturbance of interest points; using stable points of interest distribution characteristics and spatial information of high content, and the regional division of the annular convex hull image, weighted vector while using the convex hull and the annular color histogram to describe the network image features. Experiments show that the algorithm can effectively avoid the interference of unstable points of interest caused by the local the regional characteristics of the network image more accurate description, the retrieval accuracy is greatly improved.2. proposed an algorithm scale invariant interest point detection based on IPDS ( H). According to the scale and affine transform image ZTE will cause the network interest lost or false points of interest, the convolution of different scale factor of the image in multi-scale space image is obtained by pixel comparison; each multi-scale image in pixels with the adjacent scale and location of the gray extremum get multi-scale space; according to the gradient change of the neighborhood of each scale space extreme points in the value and the given threshold, extreme points larger gradient retention in the neighborhood, and the extreme point as stable interest points in scale space. The experimental results show that the algorithm of image rotation and the retrieval speed. Translation, robustness, can effectively improve the consistency of image similarity scale points of interest, so as to improve the network image points of interest in the accuracy of.3. retrieval algorithm is proposed based on salient regions based on invariant The characteristics of image retrieval algorithm (SRIF). According to the traditional division method based on interest points in the image from the point of interest is susceptible to interference problems, according to the distribution density of the image in IPDSH points of interest, using the traversal method to find a significant area, and determine the scale space significant points of interest; use of significant regional pseudo Zernike to describe the moment invariant features of the image, in order to improve the accuracy of feature description. Experiments show that the algorithm is more consistent with human visual principle, can accurately extract the salient region of image, and increase the reliability of salient points, extracting features with scale invariance, not vulnerable to the attack of noise interference, can put forward a kind of ecological selection particle swarm optimization based on feedback network effectively improve the image retrieval precision and recall rate of.4. (r/KPSO-RF) image retrieval methods. In view of the traditional algorithm in image retrieval based on relevance feedback diagram The lack of adaptive parameter adjustment scale space flexible problems, system research on the swarm intelligence algorithm r/KPSO, and the introduction of relevance feedback process; to query image as particles are initialized by r/KPSO, global optimization, fast convergence, particle motion direction fast to the optimal solution set closer to supervision and guidance in the; to adjust the feature weights according to the parameters of the optimal result, so as to improve the feedback performance. Experimental results show that the feedback of training samples is small, high real-time requirements, the training sample asymmetry and the presence of a large number of unlabeled samples, this algorithm can accurately understand user feedback real intention, effectively solve the stochastic optimization target details the network, so as to improve the performance of image retrieval.5. proposes a new intelligent network image retrieval system (WIIRS). According to the image retrieval system based on Network For further research, analysis of the various modules of the system function, proposed a new management module, by crawling module, detection module is composed of three parts of the network image retrieval system. The system crawls through the module to realize the network image collection and number, and establish the image database; extraction and optimization algorithm for image feedback this paper introduced feature description feature detection module, image feature database is set up at the same time; the effective implementation of management module is responsible for the stability of the system operation and human-computer interaction. Finally, based on the WIIRS system, introduces the pornographic image feature extraction algorithm, put forward a kind of network pornographic image filtering system (PIRF), to improve the traditional erotic image detection algorithm based on the model of skin color detection in color distortion and brightness of partial pornographic images when there is high dark false detection rate. Experimental results show that the system can effectively achieve WIIRS The network image retrieval function, PIRF system can effectively detect and filter pornographic images in the Internet, and has a high practical value.

【学位授予单位】：西安电子科技大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TP391.41

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