本文選題：相關(guān)反饋 + 定性空間推理��； 參考：《吉林大學(xué)》2012年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)技術(shù)的高速發(fā)展、各種形式的媒體不斷增加、互聯(lián)網(wǎng)上以圖像形式展現(xiàn)的內(nèi)容越來越多，圖像檢索逐步成為了主要的檢索方式之一�；ヂ�(lián)網(wǎng)上傳統(tǒng)的搜索引擎，例如Google、Yahoo！、Bing等提供的圖像檢索，都是基于圖像的文件名、所在網(wǎng)頁內(nèi)嵌的關(guān)鍵字等機(jī)制施行的查詢。由于圖像的理解與文本表達(dá)之間的鴻溝，基于關(guān)鍵字的圖像檢索的準(zhǔn)確率不高，因此，研究者們提出了基于內(nèi)容的圖像檢索(Content-BasedImage Retrieval，CBIR)�；趦�(nèi)容的圖像檢索是指查詢條件本身就是一幅圖像，或是對(duì)圖像內(nèi)容的描述，通過提取底層或者高層的特征建立索引，計(jì)算這些特征和查詢特征之間的距離，得到兩幅圖像間的相似程度。 基于空間關(guān)系相似性的圖像檢索屬于CBIR的一個(gè)分支。該方法在獲取對(duì)象的類別之后，找出在兩個(gè)圖像內(nèi)均出現(xiàn)的對(duì)象，將這些對(duì)象組成一個(gè)對(duì)象集合，判斷該對(duì)象集合內(nèi)哪些對(duì)象在兩個(gè)圖像中構(gòu)成的空間關(guān)系是一致的，將空間關(guān)系一致的對(duì)象標(biāo)志為“相似對(duì)象”。進(jìn)行圖像檢索時(shí)，用戶有時(shí)無法清楚描述目標(biāo)圖像的特征，提供的查詢圖像只具有目標(biāo)圖像的部分特征，這種情況下，系統(tǒng)需要在第一次推薦之后進(jìn)行多次相關(guān)反饋。通過用戶在推薦結(jié)果中選擇若干與目標(biāo)圖像相關(guān)的結(jié)果（即正例），利用這些正例中公共的對(duì)象、以及公共對(duì)象間一致的空間關(guān)系等有效信息使目標(biāo)圖像的特征描述不斷趨于精確，最終檢索系統(tǒng)可根據(jù)較精確的查詢特征提供目標(biāo)圖像。本文的研究工作分為兩部分： 首先，本文通過對(duì)公共模式方法(Common Pattern Method，CPM)的改進(jìn)，提出了基于矩形代數(shù)的相似性圖像檢索(Similarity Retrieval by Rectangle Algebra，SRRA)算法。SRRA算法將對(duì)象抽象成最小邊界矩形，利用矩形代數(shù)判斷對(duì)象間的空間關(guān)系。和基于CPM的算法所采用的基于點(diǎn)的空間關(guān)系模型相比，最小邊界矩形能更精確地表示出對(duì)象的空間大小、范圍等信息，而且利用矩形代數(shù)表示對(duì)象間的空間關(guān)系比CPM中采用的type-i方法更嚴(yán)格，使得最終的檢索結(jié)果更加精準(zhǔn)；此外，由于檢索過程中剪枝掉了大量冗余的對(duì)象，SRRA算法能顯著減少檢索時(shí)間。 其次，本文采用查詢特征重定義的方法，提出了基于SRRA的相關(guān)反饋處理算法。該算法首先將每幅圖像轉(zhuǎn)化為一個(gè)有序符號(hào)串，針對(duì)所有正例的符號(hào)串求得一個(gè)最長序列模式，該最長序列模式表示所有正例包含的公共對(duì)象、以及公共對(duì)象間一致的空間關(guān)系；其次求得最長序列模式與查詢圖像的符號(hào)串的最短公共超串，作為新查詢圖像的符號(hào)串；最后利用矩形代數(shù)基本關(guān)系復(fù)合、路徑相容原理相結(jié)合的方法，計(jì)算新的符號(hào)串中對(duì)象間的矩形代數(shù)關(guān)系。經(jīng)過以上三個(gè)步驟，對(duì)查詢特性進(jìn)行了重定義，進(jìn)入到下一輪反饋；當(dāng)反饋到一定次數(shù)，或者由用戶確認(rèn)終止反饋時(shí)，檢索結(jié)束。 本文采用C++與matlab混合編程的方式實(shí)現(xiàn)了基于空間關(guān)系相似性的圖像檢索系統(tǒng)。同時(shí)，針對(duì)以上兩方面的研究，本文比較了SRRA算法與CPM的檢索時(shí)間和檢索效果，給出了基于SRRA的相關(guān)反饋處理算法的實(shí)驗(yàn)結(jié)果。本文的實(shí)驗(yàn)采用自動(dòng)生成的圖像數(shù)據(jù)和來自http://wang.ist.psu.edu/docs/related/的數(shù)據(jù)庫，實(shí)驗(yàn)結(jié)果表明： 1.與CPM相比，SRRA算法不但提高了檢索結(jié)果的精確性，而且顯著縮短了檢索時(shí)間，其運(yùn)行時(shí)間比CPM減少了近50%； 2.基于SRRA的相關(guān)反饋處理算法在原始查詢圖像的特征基礎(chǔ)上，通過提取正例中的公共對(duì)象、公共對(duì)象間一致的空間關(guān)系等有效信息，不斷精化查詢圖像，使得系統(tǒng)能更清晰地刻畫目標(biāo)圖像的空間特征，從推薦結(jié)果中不斷刪除無關(guān)圖像，并最終將符合目標(biāo)特征的結(jié)果推薦給用戶。
[Abstract]:With the rapid development of Internet technology, various forms of media are increasing, and more and more images are displayed on the Internet, and image retrieval has gradually become one of the main retrieval methods. The traditional search engines on the Internet, such as Google, Yahoo!, and Bing, are all images based on the name of the image file. Because of the gap between image understanding and text expression, the accuracy of keyword based image retrieval is not high because of the gap between image understanding and text expression. Therefore, the researchers proposed Content-BasedImage Retrieval (CBIR) based on content. Content based image retrieval means the query condition itself It is an image, or a description of the content of the image. The index is established by extracting the features of the bottom or the high level, and the distance between these features and the query features is calculated, and the similarity between the two images is obtained.
Image retrieval based on spatial relation similarity belongs to a branch of CBIR. After obtaining the category of the object, this method finds out the objects that appear in the two images, and makes up a set of objects, and judges which objects in the set are in the same spatial relationship in the two images. In the case of image retrieval, the user sometimes can not clearly describe the feature of the target image. The query image provided only has some features of the target image. In this case, the system needs to carry out multiple correlation feedback after the first recommendation. The result of the target image (that is, the positive example), using the common objects in these positive examples, and the consistent spatial relationship between the public objects and other effective information, the feature description of the target image tends to be accurate, and the final retrieval system can provide the target image according to the more accurate query features. The research work of this paper is divided into two parts:
First, through the improvement of Common Pattern Method (CPM), this paper proposes a rectangular algebra based similarity image retrieval (Similarity Retrieval by Rectangle Algebra, SRRA) algorithm.SRRA algorithm to abstract the object into a minimum boundary rectangle, and uses rectangular algebra to determine the spatial relationship between objects. Compared with the point based spatial relation model, the minimum boundary rectangle can more accurately represent the space size, range and other information of the object, and the spatial relation between the objects is more strict than the type-I method used in CPM by the rectangular algebra, which makes the final retrieval result more accurate; in addition, the scissors in the retrieval process are cut. A large number of redundant objects are dropped, and SRRA algorithm can significantly reduce retrieval time.
Secondly, this paper proposes a SRRA based correlation feedback processing algorithm based on query feature redefinition. This algorithm first transforms each image into an ordered string, and the longest sequence pattern is obtained for all the positive examples. The longest sequence pattern represents all the common objects, as well as the public objects, which are included in the positive examples. The shortest common superstring of the longest sequence pattern and the symbol string of the query image is obtained as the symbol string of the new query image. Finally, the rectangular algebraic relationship between the new symbol strings is calculated by using the method of combining the basic relation of the rectangular algebra and the path compatibility principle. The above three steps are carried out. In the end, the query feature is redefined and entered into the next round of feedback; when the feedback reaches a certain number, or the user confirms the termination of feedback, the search ends.
In this paper, the image retrieval system based on spatial relationship similarity is realized by using C++ and MATLAB hybrid programming. At the same time, for the above two aspects, this paper compares the retrieval time and retrieval effect of SRRA algorithm and CPM, and gives the experimental results of the correlation feedback processing algorithm based on SRRA. The experiment of this paper is automatically generated. Image data and database from http://wang.ist.psu.edu/docs/related/, the experimental results show that:
1. compared with CPM, SRRA algorithm not only improves the accuracy of retrieval results, but also significantly reduces retrieval time, and its running time is reduced by nearly 50% compared with CPM.
2. based on the characteristics of the original query image, the SRRA based correlation feedback processing algorithm constantly refined the query images by extracting the common objects and the consistent spatial relations among the public objects, making the system more clearly depicting the spatial features of the target image and continuously deleting the unrelated images from the recommended results. And finally recommend the results that conform to the target characteristics to the users.

【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP391.41

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