基于快速密度峰值聚類的圖像檢索技術(shù)研究與應(yīng)用
發(fā)布時(shí)間：2019-05-24 08:37
【摘要】：隨著人類社會(huì)的進(jìn)步以及計(jì)算機(jī)、互聯(lián)網(wǎng)、存儲(chǔ)技術(shù)等高新科技的發(fā)展,每天都有數(shù)以億計(jì)的圖像產(chǎn)生并被通過各種渠道進(jìn)行傳播,從而導(dǎo)致數(shù)字圖像的數(shù)量一直以一種驚人的速度在增長。對于如此龐大的圖像數(shù)據(jù),如何有效地管理和檢索,并從中獲取潛在的信息及價(jià)值成為了人們亟待解決的難題。因此我們需要更加快速、準(zhǔn)確的圖像檢索方法來查詢所需要的圖像及相關(guān)信息。圖像聚類為圖像檢索提供了新的技術(shù)支持,基于聚類的圖像檢索能夠在大量圖像數(shù)據(jù)中快速、精準(zhǔn)的發(fā)掘用戶感興趣的信息。然而傳統(tǒng)應(yīng)用于圖像聚類的特征提取算法往往忽略圖像顏色的空間分布信息,且適應(yīng)性較差。因此本文通過等面積矩形環(huán)對圖像進(jìn)行劃分并計(jì)算各空間區(qū)域的相關(guān)性,并根據(jù)空間區(qū)域相關(guān)性計(jì)算各區(qū)域的重要性,將空間信息與顏色信息進(jìn)行融合。同時(shí)本文研究了快速搜索密度峰值(DP)聚類算法并對其進(jìn)行合理改進(jìn)后運(yùn)用在圖像檢索系統(tǒng)中,在保證收斂速度的同時(shí)提高了聚類精度。本文主要研究內(nèi)容及工作如下:(1)研究顏色特征提取及其量化方法,常用的顏色空間一般是基于硬件角度提出的,不能很好的與人眼感知相匹配,本次研究選取HSV顏色空間作為顏色空間模型。同時(shí)為了提高運(yùn)算速度,本文通過人眼對顏色的感知對其進(jìn)行了量化,從而便于統(tǒng)計(jì)和計(jì)算。(2)研究區(qū)域相關(guān)性計(jì)算方法,傳統(tǒng)的顏色特征提取法僅對顏色值進(jìn)行統(tǒng)計(jì)和整理,并不考慮空間分布情況,為了使顏色特征更具代表性,本文提出一種基于圖像內(nèi)容的區(qū)域相關(guān)性計(jì)算方法將空間信息與顏色特征進(jìn)行了融合,并根據(jù)區(qū)域相關(guān)性自動(dòng)調(diào)整各區(qū)域的重要性權(quán)值,提高了特征提取算法的魯棒性及普適性。(3)研究DP聚類算法的優(yōu)化方案,原本的DP聚類算法截?cái)嗑嚯x固定不變,該參數(shù)的選取在某種意義上決定著聚類算法的成效。因此到一個(gè)合適的截?cái)嗑嚯x對DP算法聚類效果有較明顯的影響。本次研究提出了一種截?cái)嗑嚯x動(dòng)態(tài)調(diào)整方案,使之在保證較快的收斂速度的同時(shí)具有較高的聚類精度。最終通過實(shí)驗(yàn)驗(yàn)證,本文提出的方法是可行的、有效的。
[Abstract]:With the progress of human society and the development of high and new technology such as computer, Internet, storage technology and so on, hundreds of millions of images are produced and disseminated through various channels every day. As a result, the number of digital images has been growing at an amazing rate. For such a large image data, how to effectively manage and retrieve, and obtain potential information and value has become an urgent problem to be solved. Therefore, we need faster and more accurate image retrieval methods to query the required images and related information. Image clustering provides new technical support for image retrieval. Image retrieval based on clustering can quickly and accurately discover the information of interest to users in a large number of image data. However, the traditional feature extraction algorithms used in image clustering often ignore the spatial distribution information of image color, and the adaptability is poor. Therefore, this paper divides the image into equal area rectangular rings and calculates the correlation of each spatial region, and calculates the importance of each region according to the spatial region correlation, and merges the spatial information and color information. At the same time, this paper studies the fast search density peak (DP) clustering algorithm and improves it reasonably in the image retrieval system, which not only ensures the convergence speed, but also improves the clustering accuracy. The main research contents and work of this paper are as follows: (1) the color feature extraction and its quantification methods are studied. The commonly used color space is generally based on the hardware point of view, which can not match the human eye perception very well. In this study, HSV color space is selected as color space model. At the same time, in order to improve the operation speed, this paper quantifies the color perception by the human eye, so as to facilitate statistics and calculation. (2) the regional correlation calculation method is studied. The traditional color feature extraction method only statistics and collates the color value, and does not consider the spatial distribution, in order to make the color feature more representative. In this paper, a regional correlation calculation method based on image content is proposed to merge spatial information with color features, and the importance weights of each region are automatically adjusted according to the regional correlation. The robustness and universality of the feature extraction algorithm are improved. (3) the optimization scheme of DP clustering algorithm is studied. the truncation distance of the original DP clustering algorithm is fixed, and the selection of this parameter determines the effectiveness of the clustering algorithm in a sense. Therefore, reaching a suitable truncation distance has obvious influence on the clustering effect of DP algorithm. In this study, a dynamic adjustment scheme of truncation distance is proposed, which not only ensures the fast convergence speed, but also has high clustering accuracy. Finally, the experimental results show that the method proposed in this paper is feasible and effective.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41
本文編號(hào)：2484722
[Abstract]:With the progress of human society and the development of high and new technology such as computer, Internet, storage technology and so on, hundreds of millions of images are produced and disseminated through various channels every day. As a result, the number of digital images has been growing at an amazing rate. For such a large image data, how to effectively manage and retrieve, and obtain potential information and value has become an urgent problem to be solved. Therefore, we need faster and more accurate image retrieval methods to query the required images and related information. Image clustering provides new technical support for image retrieval. Image retrieval based on clustering can quickly and accurately discover the information of interest to users in a large number of image data. However, the traditional feature extraction algorithms used in image clustering often ignore the spatial distribution information of image color, and the adaptability is poor. Therefore, this paper divides the image into equal area rectangular rings and calculates the correlation of each spatial region, and calculates the importance of each region according to the spatial region correlation, and merges the spatial information and color information. At the same time, this paper studies the fast search density peak (DP) clustering algorithm and improves it reasonably in the image retrieval system, which not only ensures the convergence speed, but also improves the clustering accuracy. The main research contents and work of this paper are as follows: (1) the color feature extraction and its quantification methods are studied. The commonly used color space is generally based on the hardware point of view, which can not match the human eye perception very well. In this study, HSV color space is selected as color space model. At the same time, in order to improve the operation speed, this paper quantifies the color perception by the human eye, so as to facilitate statistics and calculation. (2) the regional correlation calculation method is studied. The traditional color feature extraction method only statistics and collates the color value, and does not consider the spatial distribution, in order to make the color feature more representative. In this paper, a regional correlation calculation method based on image content is proposed to merge spatial information with color features, and the importance weights of each region are automatically adjusted according to the regional correlation. The robustness and universality of the feature extraction algorithm are improved. (3) the optimization scheme of DP clustering algorithm is studied. the truncation distance of the original DP clustering algorithm is fixed, and the selection of this parameter determines the effectiveness of the clustering algorithm in a sense. Therefore, reaching a suitable truncation distance has obvious influence on the clustering effect of DP algorithm. In this study, a dynamic adjustment scheme of truncation distance is proposed, which not only ensures the fast convergence speed, but also has high clustering accuracy. Finally, the experimental results show that the method proposed in this paper is feasible and effective.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 王華秋;王重陽;聶珍;;空間密度聚類在數(shù)字圖書館圖像檢索中的應(yīng)用[J];現(xiàn)代情報(bào);2016年02期
2 王斌;;一種基于多尺度拱高形狀描述的圖像檢索方法[J];電子學(xué)報(bào);2013年09期
3 許元飛;;基于紋理的圖像檢索算法研究[J];西安科技大學(xué)學(xué)報(bào);2013年04期
4 張鑫;溫顯斌;孟慶霞;;基于顏色特征的圖像檢索方法研究[J];計(jì)算機(jī)科學(xué);2012年11期
5 黃承慧;印鑒;侯f ;;一種結(jié)合詞項(xiàng)語義信息和TF-IDF方法的文本相似度量方法[J];計(jì)算機(jī)學(xué)報(bào);2011年05期
6 張澤寶;張健沛;楊靜;;改進(jìn)聚類的索引建立方法研究[J];計(jì)算機(jī)工程與應(yīng)用;2010年02期
7 劉麗;匡綱要;;圖像紋理特征提取方法綜述[J];中國圖象圖形學(xué)報(bào);2009年04期
8 李志欣;施智平;李志清;史忠植;;圖像檢索中語義映射方法綜述[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2008年08期
9 段立娟;;形狀特征的編碼描述研究綜述[J];計(jì)算機(jī)科學(xué);2007年08期
10 吳成玉,邰曉英,趙杰煜;基于顏色特征的圖像檢索[J];計(jì)算機(jī)應(yīng)用;2004年S1期
相關(guān)博士學(xué)位論文 前1條
1 沈項(xiàng)軍;基于語義學(xué)習(xí)的圖像檢索研究[D];中國科學(xué)技術(shù)大學(xué);2006年
相關(guān)碩士學(xué)位論文 前2條
1 楊杰;面向圖像檢索的海量圖像自動(dòng)聚類方法研究[D];北京交通大學(xué);2015年
2 楊濤;聚類算法在圖像索引中的應(yīng)用與研究[D];北京交通大學(xué);2009年
，本文編號(hào)：2484722
本文鏈接：http://www.sikaile.net/kejilunwen/ruanjiangongchenglunwen/2484722.html
最近更新
教材專著
