本文關(guān)鍵詞： 細(xì)菌覓食算法 圖像分割 圖像分類 人臉識(shí)別 參數(shù)優(yōu)化　出處：《湖南工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著計(jì)算機(jī)技術(shù)的不斷發(fā)展以及其軟硬件的更新?lián)Q代,越來越多的人開始使用計(jì)算機(jī)對(duì)圖像做各式各樣的處理。圖像處理技術(shù)也在迅速的發(fā)展,其應(yīng)用范圍也不斷拓展,例如機(jī)器人視覺以及工業(yè)檢測(cè)等。圖像處理是一門引人注目和具有遠(yuǎn)大前景的學(xué)科,包括圖像分割、圖像增強(qiáng)、圖像分類以及目標(biāo)識(shí)別等多個(gè)技術(shù)領(lǐng)域。圖像分割和圖像分類這兩方面內(nèi)容如果處理的好,能對(duì)后續(xù)工作帶來良好的結(jié)果;若是處理不好,這會(huì)對(duì)后續(xù)工作(如目標(biāo)識(shí)別)帶來不少的麻煩還有難點(diǎn),然而許多常見的分割算法和分類算法可能只解決了部分的分割和分類問題,還余下大部分問題。基于仿生算法的分割和分類算法又可以進(jìn)一步解決些問題。然而,傳統(tǒng)的仿生算法也還是有些局限性,例如比較容易陷入局部最優(yōu),效率相對(duì)比較低下,搜尋精度不足等。所以本文提出了使用改進(jìn)的細(xì)菌覓食算法來對(duì)圖像進(jìn)行分割和分類,主要研究工作如下:(1)提出了一種改進(jìn)細(xì)菌覓食算法(IBFO)并應(yīng)用于圖像分割。針對(duì)傳統(tǒng)細(xì)菌覓食算法在處理大量圖像時(shí)效率較低和精度不高的問題,在算法的遷徙行為和趨化行為都做了動(dòng)態(tài)的調(diào)整,根據(jù)所在階段改變行為方式,并用改進(jìn)算法對(duì)圖像進(jìn)行分割實(shí)驗(yàn)。通過實(shí)驗(yàn)驗(yàn)證,與基于仿生算法的聚類分析算法結(jié)果相比,并用測(cè)試函數(shù)收斂性、區(qū)域間灰度對(duì)比度、不均勻性和時(shí)間耗費(fèi)指標(biāo)來驗(yàn)證了改進(jìn)細(xì)菌覓食算法的有效性。(2)提出了另一種改進(jìn)細(xì)菌覓食算法(SIBFO)并應(yīng)用目標(biāo)圖像分類。其主要利用改進(jìn)細(xì)菌覓食算法對(duì)從視頻中提取的行人、汽車以及寵物進(jìn)行聚類分析完成圖像中的目標(biāo)分類。通過實(shí)驗(yàn)驗(yàn)證,與基于傳統(tǒng)群體智能算法的目標(biāo)分類結(jié)果相比,并用分類算法的指標(biāo)查全率、查準(zhǔn)率和綜合這兩個(gè)指標(biāo)的加權(quán)指標(biāo)來進(jìn)行驗(yàn)證了改進(jìn)細(xì)菌覓食算法的有效性。(3)將第二種改進(jìn)細(xì)菌覓食算法(SIBFO)應(yīng)用于支持向量機(jī)參數(shù)優(yōu)化方法并進(jìn)行人臉識(shí)別。根據(jù)支持向量機(jī)分類器的原理,將其與之前改進(jìn)的細(xì)菌覓食算法結(jié)合起來以達(dá)到最優(yōu)分類器的目的。然后使用結(jié)合后的分類器對(duì)人臉圖像進(jìn)行識(shí)別實(shí)驗(yàn)。通過實(shí)驗(yàn)驗(yàn)證,與基于傳統(tǒng)仿生算法的支持向量機(jī)參數(shù)優(yōu)化進(jìn)行比較,在全局搜索方面、優(yōu)化后的支持向量機(jī)的預(yù)測(cè)和誤差分析方面以及對(duì)ORL人臉庫(kù)和AR人臉庫(kù)進(jìn)行人臉識(shí)別的分類準(zhǔn)確率方面考證了改進(jìn)算法的優(yōu)越性。
[Abstract]:With the continuous development of computer technology and the upgrading of its software and hardware, more and more people begin to use computers to do various kinds of image processing. For example, robot vision and industrial detection. Image processing is an attractive and promising subject, including image segmentation, image enhancement, Image segmentation and image classification can bring good results to the subsequent work if they are handled well. This will bring a lot of trouble and difficulties to the follow-up work (such as target recognition). However, many common segmentation algorithms and classification algorithms may only solve part of the segmentation and classification problems. However, the traditional bionic algorithm has some limitations, for example, it is easy to fall into local optimum, and the efficiency is relatively low. Therefore, an improved bacterial foraging algorithm is proposed to segment and classify images. The main research work is as follows: (1) an improved bacterial foraging algorithm (IBFOFOA) is proposed and applied to image segmentation. The migration behavior and chemotaxis behavior of the algorithm are dynamically adjusted. According to the stage, the behavior is changed, and the image segmentation experiment is carried out with the improved algorithm. The experimental results are compared with the results of the clustering analysis algorithm based on the bionic algorithm. The convergence of the function and the contrast between regions are tested. Inhomogeneity and time-wasting index to verify the effectiveness of the improved bacterial foraging algorithm. (2) another improved bacterial foraging algorithm (SIBFOO) is proposed and the target image is classified. The improved bacterial foraging algorithm is mainly used to extract pedestrians from the video. The target classification in the image is completed by clustering analysis of vehicles and pets. Compared with the target classification results based on the traditional swarm intelligence algorithm, the target recall rate of the classification algorithm is compared with that of the traditional swarm intelligence algorithm. The validity of the improved bacterial foraging algorithm is verified by the precision rate and the weighted index of these two indexes. The second improved bacterial foraging algorithm (SIBFOO) is applied to the parameter optimization method of support vector machine and face recognition is carried out. According to the principle of support vector machine classifier, It is combined with the previous improved bacterial foraging algorithm to achieve the purpose of the optimal classifier. Then the combined classifier is used to carry out the recognition experiment on the face image. Compared with the support vector machine parameter optimization based on the traditional bionic algorithm, in the aspect of global search, The prediction and error analysis of the optimized support vector machine (SVM) and the classification accuracy of ORL face database and AR face database are studied to prove the superiority of the improved algorithm.
【學(xué)位授予單位】：湖南工業(yè)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

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