本文選題：空間金字塔匹配 + 上下文相關直方圖��； 參考：《浙江大學》2011年碩士論文

【摘要】：從全球來看,動漫產(chǎn)業(yè)已經(jīng)成為一個龐大的產(chǎn)業(yè)。然而在我國動漫產(chǎn)業(yè)還是一個新興產(chǎn)業(yè),起步不久但發(fā)展迅速。在動漫產(chǎn)業(yè)飛速發(fā)展的背景下,面對海量的數(shù)字化動漫素材,如何結合動漫素材的視覺特點,有效地提取圖像特征,并在此基礎上實現(xiàn)高效的分類識別成為亟待解決的問題。因此,利用數(shù)字圖像處理技術解決上述問題成為本文的研究動機。 動漫素材,主要以圖像的形式存在。剪紙圖像,作為一種典型的動漫素材,在圖像特征和人類視覺特性上都具有不同于普通圖像的特點。因此,本文的研究目標是：以剪紙圖像為主要研究對象,根據(jù)剪紙圖像上述特性,對基于剪紙圖像的特征提取與分類識別進行研究,進而為上述問題提出了解決方案。 本文首先針對中國剪紙識別中存在底層形狀特征難以表達高層語義這一“語義鴻溝”問題,提出的基于空間約束特征組合與選擇的中國剪紙識別算法將空間金字塔匹配和上下文相關直方圖這兩種圖像特征提取方法結合起來,有效地克服了其在表達圖像形狀上的局限性,并在實驗中驗證其有效性。然后針對傳統(tǒng)圖像分類識別框架由于在特征提取階段沒有引入圖像空間信息,導致圖像特征表達能力不足,從而制約了分類識別正確率的提高這一問題,在基于空間信息的中國剪紙?zhí)卣魈崛》椒ǖ幕A之上,進一步提出了基于特征選擇與組合的中國剪紙分類識別方法。最后通過實驗對比和分析驗證了基于特征選擇與組合的中國剪紙分類識別算法的有效性。
[Abstract]:From the global perspective, animation industry has become a huge industry. However, the animation industry in China is still a new industry, starting soon but rapid development. Under the background of the rapid development of animation industry, how to combine the visual characteristics of animation material, how to extract image features effectively, and how to achieve efficient classification and recognition becomes a problem to be solved urgently in the face of mass digital animation material. Therefore, the use of digital image processing technology to solve the above problems has become the motivation of this paper. Animation material, mainly in the form of images. As a typical animation material, paper-cut image has different features from ordinary images in image features and human visual characteristics. Therefore, the research goal of this paper is: take paper-cut image as the main research object, according to the above characteristics of paper-cut image, study the feature extraction and classification recognition based on paper-cut image, and then put forward the solution to the above problems. In this paper, we first aim at the problem of "semantic gap" in Chinese paper-cut recognition, in which the underlying shape features are difficult to express high-level semantics. The proposed Chinese paper-cut recognition algorithm based on spatial constraint feature combination and selection combines spatial pyramid matching and context-dependent histogram as two image feature extraction methods. It overcomes the limitation of image shape expression and proves its validity in experiments. Then because the traditional image classification recognition framework does not introduce the image spatial information in the feature extraction stage, the ability of image feature expression is insufficient, which restricts the improvement of classification recognition accuracy. Based on the feature extraction method of Chinese paper-cut based on spatial information, a Chinese paper-cut classification and recognition method based on feature selection and combination is proposed. Finally, the effectiveness of the Chinese paper-cut classification recognition algorithm based on feature selection and combination is verified by experimental comparison and analysis.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TP391.41

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本文編號：2066458