本文選題：關(guān)鍵幀提取　切入點(diǎn)：多特征融合　出處：《華中科技大學(xué)》2012年碩士論文

【摘要】：關(guān)鍵幀提取技術(shù)是基于內(nèi)容的視頻檢索核心技術(shù)之一，對(duì)于內(nèi)容復(fù)雜、場(chǎng)景繁多、動(dòng)作豐富的視頻類型（如動(dòng)畫(huà)片、廣告片、動(dòng)作片），現(xiàn)有的關(guān)鍵幀提取方法性能并不理想。第一，，關(guān)鍵幀集合代表性不夠，不能有效代表原視頻內(nèi)容；第二，關(guān)鍵幀集合存在冗余，不夠簡(jiǎn)潔。如果用這樣的關(guān)鍵幀集合對(duì)原視頻數(shù)據(jù)進(jìn)行索引，那么視頻檢索的快速性和準(zhǔn)確性將會(huì)受到很大的影響。 為應(yīng)對(duì)上述亟待解決的難點(diǎn)，有必要對(duì)視頻序列關(guān)鍵幀提取方法進(jìn)行創(chuàng)新；另一方面，由于視頻文件數(shù)據(jù)量龐大，為了增強(qiáng)實(shí)用性，有必要提高關(guān)鍵幀提取系統(tǒng)的離線計(jì)算效率。 本文針對(duì)內(nèi)容復(fù)雜、場(chǎng)景繁多、動(dòng)作豐富的視頻類型，提出了一種新的基于聚類算法的多特征融合的關(guān)鍵幀提取方法。首先，將多種特征進(jìn)行融合，再進(jìn)行相似性度量可以更加完整全面的描述內(nèi)容復(fù)雜的視頻；其次利用聚類算法依據(jù)場(chǎng)景對(duì)視頻序列進(jìn)行聚類，免去了在場(chǎng)景繁多的情況下鏡頭檢測(cè)分割的困難和繁雜；再次，依據(jù)運(yùn)動(dòng)量極小值標(biāo)準(zhǔn)來(lái)提取關(guān)鍵幀，能更準(zhǔn)確的代表動(dòng)作豐富的視頻內(nèi)容。同時(shí)本文對(duì)所采用的聚類算法進(jìn)行了改進(jìn)，提高了關(guān)鍵幀提取系統(tǒng)的離線計(jì)算效率。 本文最后設(shè)計(jì)并實(shí)現(xiàn)了關(guān)鍵幀提取系統(tǒng)，通過(guò)對(duì)不同方法的提取結(jié)果進(jìn)行定量分析與對(duì)比，表明本文提出的方法具有較高的性能和準(zhǔn)確度。本文的研究對(duì)于促進(jìn)關(guān)鍵幀提取技術(shù)和基于內(nèi)容的視頻檢索的發(fā)展應(yīng)用具有理論及應(yīng)用價(jià)值。
[Abstract]:Key frame extraction is one of the core techniques of content-based video retrieval. The performance of the existing key frame extraction methods is not ideal. First, the key frame set is not representative enough to represent the original video content effectively; second, the key frame set has redundancy. If the original video data is indexed with such a set of key frames, the speed and accuracy of video retrieval will be greatly affected. In order to deal with the above difficulties, it is necessary to innovate the method of video sequence key frame extraction. On the other hand, because of the huge amount of video file data, in order to enhance the practicability, It is necessary to improve the off-line computing efficiency of the key frame extraction system. In this paper, a new key frame extraction method of multi-feature fusion based on clustering algorithm is proposed for the video types with complex content, multi-scene and rich action. Then the similarity measurement can more complete and comprehensive description of the complex content of video. Secondly, clustering algorithm is used to cluster the video sequences according to the scene, which avoids the difficulty and complexity of shot detection and segmentation in the case of a wide range of scenes. Thirdly, The key frame can be extracted according to the minimum motion quantity standard, which can represent the rich video content more accurately. At the same time, the clustering algorithm used in this paper is improved to improve the off-line computing efficiency of the key frame extraction system. Finally, a key frame extraction system is designed and implemented, and the results of different methods are quantitatively analyzed and compared. The results show that the proposed method has high performance and accuracy. The research in this paper has theoretical and practical value for the development and application of key frame extraction technology and content-based video retrieval.
【學(xué)位授予單位】：華中科技大學(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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本文編號(hào)：1699974