本文關(guān)鍵詞： 視頻檢索 鏡頭分割 關(guān)鍵幀提取 視頻分類　出處：《沈陽大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著時(shí)代的進(jìn)步與科技的發(fā)展，浩如煙海的視頻數(shù)據(jù)表現(xiàn)了社會(huì)與生活的方方面面。如何對(duì)視頻信息進(jìn)行檢索與分類，當(dāng)前已經(jīng)成為一個(gè)迫切需要解決的課題。為了有效地從視頻媒體庫中獲得所需要的信息，必須對(duì)視頻信息進(jìn)行有效地組織與索引。因此，基于內(nèi)容的視頻檢索與分類方法研究符合社會(huì)與人們的需求。 本文針對(duì)基于內(nèi)容的視頻檢索與分類技術(shù)中的視頻數(shù)據(jù)特點(diǎn)、視頻結(jié)構(gòu)化以及關(guān)鍵技術(shù)等做出了概要論述，并著重研究了視頻檢索與分類中的鏡頭分割與關(guān)鍵幀提方法。在現(xiàn)有的視頻分割研究成果基礎(chǔ)上,提出了一種基于自適應(yīng)雙閾值的改進(jìn)算法。該算法采用權(quán)重不同的優(yōu)化分塊策略，并通過剔除一部分影響較大的幀間差值來減少了外圍因素的干擾,與改進(jìn)前算法比較，突變閾值稍有降低，提高了鏡頭邊界檢測(cè)的查全率，同時(shí)漸變閾值也稍有降低，克服了漸變中幀間差別很小的幀的影響。本文還在前人視頻關(guān)鍵幀提取的研究基礎(chǔ)上,提出了一種改進(jìn)基于互信息的視頻關(guān)鍵幀提取算法。該算法對(duì)關(guān)鍵幀數(shù)目的確定進(jìn)行了優(yōu)化，，使關(guān)鍵幀數(shù)目能夠根據(jù)視頻內(nèi)容自動(dòng)調(diào)整大小，增加了關(guān)鍵幀數(shù)目的自適應(yīng)性，并且將以前單個(gè)鏡頭的關(guān)鍵幀提取擴(kuò)展到了多個(gè)鏡頭以至于整個(gè)視頻的關(guān)鍵幀提取，最終使提取的關(guān)鍵幀更好的描述視頻內(nèi)容。同時(shí)，本文用主成分分析對(duì)提取的特征進(jìn)行降維處理，利用遺傳算法來達(dá)到SVM分類器參數(shù)優(yōu)化的目的。在保證識(shí)別精度的前提下減小特征維數(shù)，對(duì)顏色特征進(jìn)行優(yōu)化，找出更有利于準(zhǔn)確分類的特征子集。同時(shí)，優(yōu)化分類器的參數(shù)選取來提高分類器的分類準(zhǔn)確率和分類速度。本文還對(duì)視頻檢索與分類系統(tǒng)進(jìn)行模塊化設(shè)計(jì)，并分別詳細(xì)的介紹了各個(gè)模塊。 實(shí)驗(yàn)結(jié)果表明，在測(cè)試集上,本文的鏡頭邊界檢測(cè)改進(jìn)算法的平均查全率和查準(zhǔn)率均高于自適應(yīng)雙閾值算法，分別達(dá)到了87.86%和93.91%,取得了很好的鏡頭邊界檢測(cè)效果。本文的關(guān)鍵幀提取改進(jìn)算法針對(duì)總幀數(shù)為12470，鏡頭數(shù)為115的動(dòng)漫視頻進(jìn)行關(guān)鍵幀提取，提取了21個(gè)視頻幀為關(guān)鍵幀，而未改進(jìn)算法針對(duì)總幀數(shù)為605，鏡頭數(shù)為11的動(dòng)漫視頻進(jìn)行關(guān)鍵幀提取，提取了13幀作為關(guān)鍵幀，上述數(shù)據(jù)表明，本算法提取的關(guān)鍵幀，可以有效地概括視頻的內(nèi)容，并且提高提高了提取效率，減少了一定的關(guān)鍵幀冗余。本文最后對(duì)研究工作進(jìn)行總結(jié),提出下一步工作的努力方向。
[Abstract]:With the progress of the times and the development of science and technology, the vast amount of video data shows all aspects of society and life. How to retrieve and classify video information, In order to obtain the needed information from video media library effectively, it is necessary to organize and index the video information effectively. Content-based video retrieval and classification methods meet the needs of society and people. In this paper, the characteristics of video data in content-based video retrieval and classification technology, video structure and key technologies are briefly discussed. The methods of shot segmentation and key frame extraction in video retrieval and classification are studied. An improved algorithm based on adaptive double threshold is proposed. The algorithm adopts optimized block strategy with different weights, and reduces the interference of peripheral factors by eliminating some significant inter-frame differences, compared with the improved algorithm. The abrupt threshold is reduced slightly, the recall rate of shot boundary detection is improved, and the gradient threshold is reduced slightly, which overcomes the influence of the frame with little difference between frames. This paper also based on the research of key frame extraction in previous video. An improved video key-frame extraction algorithm based on mutual information is proposed, in which the key frame number is optimized, and the key frame number can be automatically resized according to the video content, thus increasing the self-adaptability of the key frame number. And the key frame extraction of the previous single shot is extended to multiple shots so that the key frame of the whole video can be extracted so that the extracted key frame can describe the video content better. At the same time, In this paper, principal component analysis (PCA) is used to reduce the dimension of the extracted features, and genetic algorithm is used to optimize the parameters of the SVM classifier. Under the premise of ensuring the recognition accuracy, the feature dimension is reduced, and the color features are optimized. At the same time, the parameter selection of classifier is optimized to improve the classification accuracy and classification speed. This paper also designs the video retrieval and classification system modularized. Each module is introduced in detail. The experimental results show that the average recall and precision of the improved shot boundary detection algorithm are higher than that of the adaptive double threshold algorithm in the test set. In this paper, the improved key frame extraction algorithm is used to extract key frames for animation video with 12470 frames and 115 shots, and 21 video frames are extracted as key frames. However, the unimproved algorithm extracts the key frames of the animation video with 605 frames and 11 shots, and extracts 13 frames as the key frames. The above data show that the key frames extracted by this algorithm can effectively generalize the content of the video. It also improves the extraction efficiency and reduces the key frame redundancy. Finally, this paper summarizes the research work and puts forward the direction of the next work.
【學(xué)位授予單位】：沈陽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP391.41

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