本文選題：外觀設(shè)計專利圖像 + 圖像視覺特征��； 參考：《廣東工業(yè)大學(xué)》2013年碩士論文

【摘要】：隨著知識經(jīng)濟和經(jīng)濟全球化深入發(fā)展,知識產(chǎn)權(quán)日益成為國家發(fā)展的戰(zhàn)略性資源和國際競爭力的核心要素。外觀設(shè)計專利作為知識產(chǎn)權(quán)的一項重要內(nèi)容,我國政府、企業(yè)對外觀設(shè)計專利的保護越來越重視。通常專利圖像數(shù)據(jù)庫都是海量的,因此開發(fā)并發(fā)展基于內(nèi)容的外觀專利圖像檢索系統(tǒng)是十分必要的,同時具有深遠、重大的社會、經(jīng)濟效益。 外觀專利圖像檢索系統(tǒng)在檢索過程中往往只是簡單的比較圖像視覺特征之間的相似度,并沒有按語義檢索圖像。而且圖像庫中的圖像通常是海量的,順序檢索的計算量十分巨大,也是十分耗時的。針對以上問題,將這些圖像劃分為一些有意義的類別成為越來越迫切的需求,即實現(xiàn)自動分類。自動分類不但能滿足用戶根據(jù)圖像語義內(nèi)容檢索的要求,還能提高檢索速度。因此,圖像根據(jù)語義分類是一個值得深入研究的領(lǐng)域。 本文以外觀專利圖像的邊緣輪廓距離作為基礎(chǔ)數(shù)據(jù),在兼顧外觀專利圖像語義相似和低層特征相似時,分別使用支持向量機(SVM,Support Vector Machine)、K均值聚類、NJW譜聚類對外觀專利圖像分類,并提出一種基于均值的譜聚類特征向量選擇算法。針對上面四種分類算法,設(shè)計了一整套實驗方案用來外觀專利圖像分類。實驗表明,當圖像庫的數(shù)據(jù)量較小時,四種算法的分類效果較差,但隨著數(shù)據(jù)量的增大,分類準確率得到明顯的改善,并趨于穩(wěn)定的狀態(tài)。 在簡要介紹外觀專利檢索技術(shù)和圖像分類方法現(xiàn)狀的基礎(chǔ)上,論文主要做了以下三個方面工作： (1)闡述了支持向量機的基本思想和分類器的構(gòu)造,并將外觀專利圖像特征數(shù)據(jù)作為分類器的輸入,實現(xiàn)自動分類。 (2)在兼顧外觀專利圖像語義相似和低層特征相似時,介紹使用K均值聚類算法實現(xiàn)外觀專利圖像分類的步驟。 (3)介紹了譜聚類的基本原理和實現(xiàn)步驟,提出基于均值的譜聚類特征向量選擇算法,并將外觀專利圖像特征數(shù)據(jù)作為試驗的數(shù)據(jù)集,驗證K均值聚類算法、NJW譜聚類算法和基于均值的譜聚類特征向量選擇算法在該數(shù)據(jù)集上分類的有效性。同時在相同特征數(shù)據(jù)的情況下,分析了不同分類方法對圖像分類效果的影響。
[Abstract]:With the development of knowledge economy and economic globalization, intellectual property has become the strategic resource of national development and the core element of international competitiveness. As an important part of intellectual property, our government and enterprises pay more and more attention to the protection of design patents. The patent image database is usually massive, so it is necessary to develop and develop the content-based appearance patent image retrieval system, which has far-reaching, significant social and economic benefits. In the process of image retrieval, the appearance patent image retrieval system only simply compares the similarity between the visual features of the image, and does not retrieve the image according to the semantics. Moreover, the images in the image database are usually massive, and the computation of sequential retrieval is very large and time-consuming. To solve the above problems, it is more and more urgent to divide these images into some meaningful categories, that is, to realize automatic classification. Automatic classification can not only meet the requirements of image semantic content retrieval, but also improve the retrieval speed. Therefore, image classification based on semantics is an area worthy of further study. In this paper, the edge contour distance of patented appearance image is taken as the basic data. When semantic similarity and low-level feature similarity are taken into account, support vector machine (SVM) support Vector machine is used to classify the patented appearance image by NJW spectrum clustering. A spectral clustering feature vector selection algorithm based on mean value is proposed. Aiming at the above four classification algorithms, a set of experimental schemes are designed for image classification of patented appearance. The experimental results show that the classification effect of the four algorithms is poor when the amount of data in the image database is small, but with the increase of the amount of data, the classification accuracy is obviously improved and tends to be stable. On the basis of a brief introduction to the present situation of patent retrieval technology and image classification methods, the thesis mainly does the following three aspects: In this paper, the basic idea of support vector machine and the construction of classifier are expounded, and the feature data of appearance patent image are taken as the input of classifier to realize automatic classification. This paper introduces the steps of applying K-means clustering algorithm to realize the classification of patented appearance images by taking into account the semantic similarity and low-level feature similarity of appearance patent images. In this paper, the basic principle and implementation steps of spectral clustering are introduced, and a spectral clustering feature vector selection algorithm based on mean value is proposed, and the feature data of patented appearance image is taken as the experimental data set. The validity of the K-means clustering algorithm, NJW spectral clustering algorithm and the mean-based spectral clustering feature vector selection algorithm on the data set is verified. At the same time, the effect of different classification methods on image classification is analyzed under the same feature data.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TP391.41

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