發(fā)布時(shí)間：2018-02-14 05:46

  本文關(guān)鍵詞： 圖像檢索 底層特征融合 特征降維 位平面熵 多矩結(jié)構(gòu)描述符　出處：《山東師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著Flickr、Facebook等社交網(wǎng)站的流行,圖像資源正在以驚人的速度不斷增長,如何從海量的圖像中快速有效地提取用戶所需要的資源已成為人們工作和生活中必須解決的關(guān)鍵問題。因此,不少學(xué)者進(jìn)行了卓有成效的研究,尤其是在對圖像中的單一底層特征或者綜合特征的提取算法方面取得了豐碩成果。遺憾的是,單一底層特征算法只提取一種圖像特征,如顏色、紋理、形狀等,這屬于部分特性,忽略了全局空間信息,將導(dǎo)致檢索結(jié)果不準(zhǔn)確。綜合特征算法大多是從查詢圖像中直接提取所需的特征,未考慮特征之間的關(guān)聯(lián)性,具有維數(shù)高、計(jì)算復(fù)雜的缺點(diǎn)。為解決上述問題,同時(shí)結(jié)合人類視覺機(jī)制對圖像空間特性的感知特點(diǎn),提出將圖像中的底層特征進(jìn)行有效地融合,使融合后的特征盡可能多地表達(dá)圖像信息,從而提高檢索精度和效率。具體創(chuàng)新性研究成果如下:(1)在圖像底層特征提取過程中,往往存在大量的高維特征,這些高維特征不僅增加了計(jì)算復(fù)雜度,而且在一定程度上影響底層特征的準(zhǔn)確性。為此,本文在不影響圖像表達(dá)效果的前提下,在底層特征提取過程中采用對圖像特征量化的方法進(jìn)行降維,從而提高了圖像的檢索效率。(2)人類視覺感知機(jī)制對圖像的空間結(jié)構(gòu)信息較為敏感,尤其是對圖像中分層信息和角度變化信息。針對這一特性,本文提出一種對顏色特征進(jìn)行分層、對紋理基元特征進(jìn)行角度變化,然后將兩種特征變化相融合的算法。首先,顏色特征微觀部分利用顏色直方圖,刻畫每種顏色的像素和占整個(gè)圖像總像素和的比例;宏觀部分應(yīng)用位平面熵對顏色特征進(jìn)行分層,取特征較明顯的前4層,并對每層的位平面熵加權(quán);然后,根據(jù)5個(gè)方向不同的基本結(jié)構(gòu)基元中各像素點(diǎn)的顏色值和角度值的統(tǒng)計(jì)信息,結(jié)合顏色特征,實(shí)現(xiàn)圖像檢索。實(shí)驗(yàn)結(jié)果表明,融合后的算法能有效的表達(dá)圖像空間結(jié)構(gòu)信息,提高了準(zhǔn)確率和召回率。(3)由于局部特征更能表現(xiàn)圖像的細(xì)節(jié),同時(shí)根據(jù)顏色、紋理各自的特點(diǎn)和對圖像表達(dá)所起的作用,提出了一種新的圖像特征描述子,命名為多矩結(jié)構(gòu)描述符(Multi-Rectangle Structure Descriptor,MRSD)。MRSD基于紋理基元的空間結(jié)構(gòu)定義了3種結(jié)構(gòu)描述符,用一種7-4-2-1加權(quán)方法突出每種結(jié)構(gòu)描述符在特征表達(dá)中所起作用的不同,這樣更能深刻地表達(dá)局部特征的重要性。同時(shí)融合了更契合人類視覺感知機(jī)制的HSV顏色模型,能有效地表達(dá)底層顏色信息。實(shí)驗(yàn)結(jié)果表明,將HSV顏色模型和MRSD融合能夠更好地表達(dá)圖像特征信息,解決了單一特征表達(dá)信息量不足的問題。
[Abstract]:With the popularity of social networking sites such as Flickr-Facebook and other social networking sites, image resources are growing at an alarming rate. How to quickly and effectively extract the resources needed by users from massive images has become a key problem in people's work and life. Many scholars have carried out fruitful research, especially in the extraction algorithm of single bottom feature or synthetic feature in the image. Unfortunately, the single bottom feature algorithm only extracts one image feature. Such as color, texture, shape and so on, which are part of the characteristics, ignoring the global spatial information, will lead to inaccurate retrieval results. In order to solve the above problems and combine the perception of human visual mechanism to the spatial characteristics of images, we propose to fuse the underlying features of images effectively. The fusion features can express image information as much as possible, so as to improve the retrieval accuracy and efficiency. The specific innovative research results are as follows: 1) in the process of image bottom feature extraction, there are often a large number of high-dimensional features. These high-dimensional features not only increase the computational complexity, but also affect the accuracy of the underlying features to some extent. In the process of feature extraction, the method of image feature quantization is used to reduce the dimension, which improves the retrieval efficiency of the image. (2) the human visual perception mechanism is sensitive to the spatial structure information of the image. Especially for the layered information and angle change information in the image, this paper proposes an algorithm to layer the color feature, change the angle of the texture primitive feature, and merge the two features. The microscopic part of the color feature uses the color histogram to depict the proportion of the pixel sum of each color to the total pixel sum of the whole image. Then, according to the statistical information of color value and angle value of each pixel in five basic structure primitives with different directions, the image retrieval is realized by combining the color features. The experimental results show that, The fusion algorithm can effectively express the spatial structure information of the image, improve the accuracy and recall rate. The local features can represent the details of the image more effectively. At the same time, according to the characteristics of the color, texture and the function of the image expression, the fusion algorithm can express the spatial structure information of the image effectively, improve the accuracy and recall rate. In this paper, a new image feature descriptor named Multi-Rectangle Structure descriptor MRSDG. MRSD is proposed. Three kinds of structure descriptors are defined based on texture primitives. A 7-4-2-1 weighting method is used to highlight the different functions of each structure descriptor in feature representation, which can express the importance of local features more profoundly. Meanwhile, the HSV color model, which is more compatible with human visual perception mechanism, is fused. Experimental results show that the fusion of HSV color model and MRSD can better express image feature information and solve the problem of lack of information in single feature expression.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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相關(guān)博士學(xué)位論文 前1條

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本文編號：1510011