【摘要】：隨著互聯(lián)網(wǎng)的迅猛發(fā)展,海量的數(shù)據(jù)信息與人們的生活緊密相關,圖片、視頻等多媒體信息迅速增加。如何從海量的信息庫中準確、高效的搜索出所需的信息是信息化時代的熱點問題。傳統(tǒng)的搜索以文字為搜索對象,通過關鍵字、關鍵詞來實現(xiàn)信息搜索,基于文字的搜索技術已經(jīng)非常成熟。然而文字搜索的缺陷在于,無法搜索一些很難用文字描述的圖片信息,并且文字很難直觀全面的表達人們的搜索意圖�；趦热莸膱D像檢索(Content Based Image Retrieval,CBIR)技術就能夠很好的解決這個問題�；趦热莸膱D像檢索技術通過上傳圖片來代替文字搜索,計算機自動提取圖像的特征,然后從圖像庫中找出特征相似的圖像。目前,基于內容的圖像檢索技術需要改進的主要問題是提升搜索效率和減小“語義鴻溝”以提升搜索準確率。本文以基于內容的圖像檢索為基礎做出了以下幾方面的工作:(1)提取圖像特征構建圖像特征庫,建立基于內容的圖像檢索系統(tǒng)。本文以corel1000為圖像庫,提取了圖像的顏色矩、顏色相關圖特征以及LBP紋理特征,組成特征向量庫,并采用MATLAB為工具,建立基于內容的圖像檢索系統(tǒng),實現(xiàn)了通過上傳圖片來搜索相關圖片的功能。(2)提出一種基于內容和布谷鳥算法的圖像檢索算法,將連續(xù)空間尋優(yōu)的布谷鳥搜索算法應用于離散的圖像特征空間進行圖像搜索,提高了CBIR系統(tǒng)的搜索效率。布谷鳥搜索算法(CuckooSearch,CS),也叫杜鵑搜索,是由劍橋大學YANG等在2009年提出的一種群智能優(yōu)化算法,該算法參數(shù)少、搜索路徑較好、有較強的全局搜索能力。本文將CS算法應用到基于內容圖像檢索系統(tǒng)中,將圖像搜索問題看成尋找最優(yōu)解問題,利用CS算法搜索路徑較好、有較強的全局搜索能力的優(yōu)點在圖像特征空間尋優(yōu),最后通過實驗證明了該算法比遍歷搜索算法在基于圖像檢索系統(tǒng)中有更高的搜索效率。(3)提出一種基于布谷鳥搜索動態(tài)調整支持向量機參數(shù)的相關反饋算法,減小了基于內容的圖像檢索系統(tǒng)中的“語義鴻溝”。首先,將相關反饋問題當作二分類問題,采用支持向量機(Support Vector Machine,SVM)通過反饋結果對圖像進行二分類,并通過CS算法動態(tài)搜索最佳SVM參數(shù),根據(jù)每次反饋結果自適應調整支持向量機參數(shù)。通過實驗證明該算法比傳統(tǒng)的布谷鳥搜索算法、粒子群算法(Particle Swarm Optimization,PSO)以及遺傳算法(Genetic Algorithm,GA)讓支持向量機更快更準確的實現(xiàn)分類,從而使得圖像檢索的相關反饋能在更少的反饋次數(shù)下得到更高的準確率,提高了搜索準確率。
[Abstract]:With the rapid development of the Internet, massive data and information are closely related to people's lives, pictures, video and other multimedia information is increasing rapidly. How to search the needed information accurately and efficiently from the massive information database is a hot issue in the information age. The traditional search takes the text as the search object, realizes the information search through the keyword, the text based search technology has been very mature. However, the defect of text search is that it is impossible to search for some image information that is difficult to describe in words, and it is difficult for text to express people's search intention directly and comprehensively. Content-Based Image Retrieval (Content Based Image Retrieval,CBIR) technology can solve this problem well. Content-Based Image Retrieval (CBIR) replaces text search by uploading images. The computer automatically extracts the features of the images and then finds the images with similar features from the image database. At present, the main problems that need to be improved in content-based image retrieval technology are to improve the search efficiency and reduce the "semantic gap" in order to improve the search accuracy. In this paper, based on content-based image retrieval, the following works have been done: (1) extracting image features to construct image signature database and establishing content-based image retrieval system. In this paper, the color moment, color correlation image feature and LBP texture feature of the image are extracted by using corel1000 as the image database, and the feature vector library is formed, and the content-based image retrieval system is established by using MATLAB as the tool. The function of searching related images by uploading pictures is realized. (2) an image retrieval algorithm based on content and cuckoo algorithm is proposed. The Cuckoo search algorithm with continuous space optimization is applied to the discrete image feature space for image search, which improves the search efficiency of CBIR system. Cuckoo search algorithm (CuckooSearch,CS), also called cuckoo search, is a population intelligent optimization algorithm proposed by YANG of Cambridge University in 2009. The algorithm has few parameters, good search path and strong global search ability. In this paper, the CS algorithm is applied to the content-based image retrieval system, and the image search problem is regarded as the problem of finding the optimal solution. The advantage of the CS algorithm is that the search path is better and the global search ability is stronger. Finally, experiments show that the algorithm is more efficient than the traversal search algorithm in image retrieval system. (3) A correlation feedback algorithm based on cuckoo search to dynamically adjust support vector machine parameters is proposed. The semantic gap in content-based image retrieval system is reduced. Firstly, the correlation feedback problem is regarded as a two-classification problem. The support vector machine (Support Vector Machine,SVM) is used to classify the images by feedback results, and the optimal SVM parameters are dynamically searched by the CS algorithm. The parameters of support vector machine are adaptively adjusted according to the result of each feedback. Experimental results show that the proposed algorithm makes the classification faster and more accurate than the traditional Cuckoo search algorithm, particle swarm optimization (Particle Swarm Optimization,PSO) and genetic algorithm (Genetic Algorithm,GA). Thus, the correlation feedback of image retrieval can get higher accuracy with less feedback, and improve the search accuracy.
【學位授予單位】：南昌航空大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP18

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本文編號：2369013