發(fā)布時(shí)間：2018-02-28 22:20

  本文關(guān)鍵詞： 圖像處理 網(wǎng)絡(luò)圖像檢索 特征提取 顯著區(qū)域 穩(wěn)定興趣點(diǎn) 優(yōu)化反饋　出處：《西安電子科技大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：網(wǎng)絡(luò)圖像檢索技術(shù)是信息檢索的一個(gè)重要內(nèi)容,也是當(dāng)前圖像處理和計(jì)算機(jī)視覺領(lǐng)域中的一個(gè)研究熱點(diǎn)。該技術(shù)通過提取和分析網(wǎng)絡(luò)圖像的視覺特征,為用戶提供相關(guān)的網(wǎng)絡(luò)圖像檢索服務(wù)。其主要目的是克服基于關(guān)鍵詞的檢索方式的約束,有效幫助用戶在海量信息中更快、更準(zhǔn)地搜索出所需信息,實(shí)現(xiàn)“所見即所搜”的功能,它已在網(wǎng)絡(luò)圖像搜索引擎、商標(biāo)檢索、網(wǎng)絡(luò)數(shù)字博物館以及電子商務(wù)等領(lǐng)域得到廣泛應(yīng)用。目前網(wǎng)絡(luò)圖像檢索仍存在著許多亟待解決的問題。如網(wǎng)絡(luò)圖像采用多種壓縮格式來(lái)存儲(chǔ)數(shù)據(jù),容易導(dǎo)致圖像細(xì)節(jié)信息的缺失,使得圖像圖像特征提取準(zhǔn)確度降低;不同格式的網(wǎng)絡(luò)圖像構(gòu)成的網(wǎng)絡(luò)圖像數(shù)據(jù)庫(kù)規(guī)模巨大,且具有極高的復(fù)雜性和多樣性,使得網(wǎng)絡(luò)圖像檢索實(shí)時(shí)性降低。論文全面分析了基于視覺特征的圖像檢索方法,重點(diǎn)研究了圖像局部特征提取算法以及相關(guān)反饋算法,針對(duì)網(wǎng)絡(luò)圖像檢索中各個(gè)關(guān)鍵環(huán)節(jié),提出了基于穩(wěn)定興趣點(diǎn)空域劃分、基于顯著區(qū)域不變特征和基于生態(tài)選擇粒子群優(yōu)化反饋的圖像檢索算法,并通過大量實(shí)驗(yàn)對(duì)提出算法進(jìn)行了驗(yàn)證。最后,提出一種新的智能網(wǎng)絡(luò)圖像檢索系統(tǒng),并將論文提出的特征提取算法和優(yōu)化反饋方法引入其中。論文的主要工作及貢獻(xiàn)如下:1.提出了一種基于穩(wěn)定興趣點(diǎn)空域劃分的圖像檢索算法(SIPRD)。針對(duì)傳統(tǒng)的興趣點(diǎn)檢測(cè)器在檢測(cè)網(wǎng)絡(luò)圖像時(shí)常會(huì)出現(xiàn)點(diǎn)位置偏差或誤檢測(cè)問題,對(duì)基于灰度的興趣點(diǎn)檢測(cè)算法性能及優(yōu)缺點(diǎn)進(jìn)行了分析,引入了一種基于優(yōu)化梯度濾波(ODF)的興趣點(diǎn)檢測(cè)器來(lái)檢測(cè)尺度歸一化圖像的穩(wěn)定興趣點(diǎn),以降低不穩(wěn)定興趣點(diǎn)的干擾;利用穩(wěn)定興趣點(diǎn)高信息含量特性及其空間分布規(guī)律,對(duì)圖像進(jìn)行環(huán)形和凸包區(qū)域劃分,同時(shí)使用凸包和環(huán)形顏色直方圖的加權(quán)矢量來(lái)描述網(wǎng)絡(luò)圖像特征。實(shí)驗(yàn)表明,該算法能有效避免不穩(wěn)定興趣點(diǎn)帶來(lái)的干擾,使網(wǎng)絡(luò)圖像局部區(qū)域特征的描述更為準(zhǔn)確,檢索的準(zhǔn)確度大幅提高。2.提出了一種基于尺度不變特性的興趣點(diǎn)檢測(cè)算法(IPDSH)。針對(duì)尺度變換和仿射變換會(huì)造成網(wǎng)絡(luò)圖像中興趣點(diǎn)丟失或出現(xiàn)偽興趣點(diǎn)的問題,對(duì)圖像進(jìn)行不同尺度因子的卷積,以得到圖像的多尺度空間;通過比較多尺度空間圖像中每個(gè)像素同其相鄰尺度和位置的像素的灰度,得到多尺度空間的極值點(diǎn);根據(jù)每個(gè)尺度空間極值點(diǎn)鄰域內(nèi)的梯度變化值的大小以及給定的閾值,保留鄰域內(nèi)的梯度變化值較大的極值點(diǎn),并將該極值點(diǎn)看做尺度空間中穩(wěn)定的興趣點(diǎn)。實(shí)驗(yàn)表明,該算法檢索速度快,對(duì)圖像旋轉(zhuǎn)、平移具有魯棒性,能有效提高相似圖像尺度興趣點(diǎn)的一致性,從而提高基于興趣點(diǎn)的網(wǎng)絡(luò)圖像檢索算法準(zhǔn)確度。3.提出了一種基于顯著區(qū)域不變特征的圖像檢索算法(SRIF)。針對(duì)傳統(tǒng)基于興趣點(diǎn)的區(qū)域劃分方法易受圖像中游離興趣點(diǎn)干擾的問題,根據(jù)圖像中IPDSH興趣點(diǎn)的分布密度,采用遍歷的方法來(lái)尋找顯著區(qū)域,并確定尺度空間的顯著興趣點(diǎn);使用顯著區(qū)域內(nèi)偽澤尼克矩來(lái)描述圖像的不變特征,以提高特征描述的準(zhǔn)確度。實(shí)驗(yàn)表明,該算法更加符合人眼視覺原理,能準(zhǔn)確提取圖像的顯著區(qū)域,同時(shí)增加顯著點(diǎn)的可靠性,提取的特征具有尺度不變性,不易受噪聲攻擊干擾,能有效提高網(wǎng)絡(luò)圖像檢索的查準(zhǔn)率和查全率。4.提出了一種基于生態(tài)選擇粒子群優(yōu)化反饋(r/KPSO-RF)的圖像檢索方法。針對(duì)傳統(tǒng)基于相關(guān)反饋的圖像檢索算法在調(diào)整參數(shù)尺度時(shí)缺乏靈活的自適應(yīng)調(diào)整空間的問題,系統(tǒng)研究了群體智能算法r/KPSO,并將其引入相關(guān)反饋過程;將待查詢圖像看做粒子來(lái)進(jìn)行初始化,利用r/KPSO全局尋優(yōu)、快速收斂的特點(diǎn),在理想監(jiān)督下指導(dǎo)粒子運(yùn)動(dòng)方向快速向最優(yōu)解集靠攏;根據(jù)最優(yōu)結(jié)果對(duì)特征權(quán)值參數(shù)進(jìn)行自適應(yīng)的調(diào)整,從而改善反饋性能。實(shí)驗(yàn)表明,在反饋訓(xùn)練樣本少、實(shí)時(shí)性要求高、訓(xùn)練樣本不對(duì)稱以及存在大量的未標(biāo)記樣本時(shí),該算法能準(zhǔn)確理解用戶反饋的真實(shí)意圖,有效解決了優(yōu)化目標(biāo)細(xì)節(jié)的隨機(jī)性,從而改善網(wǎng)絡(luò)圖像檢索性能。5.提出了一種新的網(wǎng)絡(luò)圖像智能檢索系統(tǒng)(WIIRS)。針對(duì)面向網(wǎng)絡(luò)的圖像檢索系統(tǒng)進(jìn)行深入研究,分析了系統(tǒng)中各個(gè)模塊的功能,提出了一種新的由抓取模塊、管理模塊、檢測(cè)模塊三部分組成網(wǎng)絡(luò)圖像檢索系統(tǒng)。該系統(tǒng)通過抓取模塊來(lái)實(shí)現(xiàn)網(wǎng)絡(luò)圖像的收集和編號(hào),同時(shí)建立圖像數(shù)據(jù)庫(kù);檢測(cè)模塊中引入本文特征提取和優(yōu)化反饋算法來(lái)進(jìn)行圖像特征描述,同時(shí)建立圖像特征庫(kù);管理模塊負(fù)責(zé)系統(tǒng)的穩(wěn)定運(yùn)行和人機(jī)交互的有效實(shí)現(xiàn)。最后,在WIIRS系統(tǒng)基礎(chǔ)上,引入了色情圖像特征提取算法,提出一種網(wǎng)絡(luò)色情圖像過濾系統(tǒng)(PIRF),以改善傳統(tǒng)基于膚色模型的色情圖像檢測(cè)算法在檢測(cè)色彩失真和亮度偏暗的色情圖像時(shí)存在較高誤檢率的問題。實(shí)驗(yàn)表明,WIIRS系統(tǒng)可以有效實(shí)現(xiàn)網(wǎng)絡(luò)圖像檢索功能,PIRF系統(tǒng)能有效地檢測(cè)和過濾互聯(lián)網(wǎng)中色情圖像,具有較高的實(shí)用價(jià)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.

【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP391.41

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