本文選題：場景分類 + 視覺詞包　； 參考：《太原科技大學》2016年碩士論文

【摘要】：隨著大數(shù)據(jù)時代的來臨,互聯(lián)網(wǎng)上圖像數(shù)據(jù)正在爆炸式增長,面對越來越多的圖像數(shù)據(jù),傳統(tǒng)的人工方式標注圖像已無法滿足實際需求,尋找一種快速自動標注圖像方法成為需要研究的主要內(nèi)容之一。目前,圖像場景分類是圖像語義自動標注的一個研究熱點,視覺詞包模型是圖像場景內(nèi)容表達的一種重要方式,但由于視覺詞包模型形成過程中會產(chǎn)生冗余視覺單詞、“多義詞”和“同義詞”,這些視覺單詞的存在嚴重影響了圖像場景的分類性能。二進制分辨矩陣方法是粗糙集屬性約簡中的一種有效方法,本文將二進制分辨矩陣與視覺詞包模型相結合,對視覺單詞的約簡和場景分類方法進行了研究,主要的研究內(nèi)容如下:(1)給出了一種基于二進制分辨矩陣的冗余視覺單詞約簡方法。該方法首先通過調(diào)整歸一化閾值α的取值,對所有訓練圖像產(chǎn)生不同的0-1信息決策表和構造不同的二進制分辨矩陣;然后以二進制分辨矩陣行列方向上1的個數(shù)作為啟發(fā)信息識別核視覺單詞和重要視覺單詞;并以這些視覺單詞作為描述圖像場景分類的決策規(guī)則,從而減少了冗余視覺單詞對圖像場景分類的影響,進而提高了圖像場景分類精度。最后在OT庫8類圖像數(shù)據(jù)集上進行實驗,驗證了該方法是有效的。(2)給出了一種基于二進制分辨矩陣的多義視覺單詞約簡方法。由于在(1)方法中歸一化閾值α的選取對決策規(guī)則的生成影響較大,而且隨著視覺單詞容量增大,刪除視覺單詞過多導致決策規(guī)則區(qū)分力度下降,因此針對任意兩類不同訓練圖像形成0-1信息決策表并構建二進制分別矩陣;然后根據(jù)二進制分辨矩陣約簡算法,將其中一類圖像分別與其它不同類圖像的約簡視覺單詞求并集運算,并以這個并集作為決定這一類圖像的決策規(guī)則,從而減少了任意兩類圖像視覺詞包中存在的“多義詞”問題,進而形成區(qū)分能力更強的決策規(guī)則。最后在OT庫8類和Fei-Fei Dataset 13類圖像數(shù)據(jù)集進行實驗,驗證了該方法的有效性。(3)開發(fā)了一個基于二進制分辨矩陣的圖像場景分類原型系統(tǒng)。基于研究內(nèi)容(1),以Matlab和Java作為開發(fā)工具,設計并實現(xiàn)了一個基于二進制分辨矩陣的圖像場景分類原型系統(tǒng)。
[Abstract]:With the advent of big data era, the image data on the Internet is increasing explosively. In the face of more and more image data, the traditional manual method can no longer meet the actual needs. Finding a fast and automatic image tagging method has become one of the main research topics. At present, image scene classification is a hot topic in image semantic automatic tagging. Visual word packet model is an important way to express the content of image scene, but in the process of forming visual word packet model, redundant visual words will be produced. The existence of "polysemy" and "synonym" seriously affects the performance of image scene classification. Binary resolution matrix method is an effective method in attribute reduction of rough set. This paper combines binary discernibility matrix with visual word packet model to study the reduction of visual words and the method of scene classification. The main research contents are as follows: (1) A redundant visual word reduction method based on binary resolution matrix is presented. Firstly, by adjusting the value of normalized threshold 偽, the method produces different 0-1 information decision tables for all training images and constructs different binary resolution matrices. Then, the number of 1 in the column direction of binary discernment matrix is used as the heuristic information to recognize the visual words and important visual words, and these visual words are used as the decision rules to describe the classification of image scene. Thus, the influence of redundant visual words on image scene classification is reduced, and the accuracy of image scene classification is improved. Finally, an experiment is carried out on 8 kinds of image data sets in OT library, which proves that this method is effective.) A polysemous visual word reduction method based on binary resolution matrix is presented. Because the selection of normalized threshold 偽 has a great influence on the generation of decision rules, and with the increase of visual word capacity, too much deletion of visual words leads to the decrease of decision rule differentiation. Therefore, the 0-1 information decision table is formed for any two kinds of different training images and binary separate matrix is constructed. Then, according to the binary resolution matrix reduction algorithm, One of the images is merged with the reduced visual words of the other kinds of images, and the union is taken as the decision rule to determine this kind of image. Thus, the problem of polysemous words in any two kinds of image visual word packets is reduced, and a more discriminative decision rule is formed. Finally, an image scene classification prototype system based on binary resolution matrix is developed by experiments on 8 classes of OT database and 13 classes of Fei-Fei Dataset data sets, which verify the effectiveness of this method. A prototype image scene classification system based on binary resolution matrix is designed and implemented based on Matlab and Java.
【學位授予單位】：太原科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41

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