發(fā)布時(shí)間：2018-10-26 21:42

【摘要】：隨著多媒體和網(wǎng)絡(luò)技術(shù)的迅速發(fā)展，多媒體信息逐漸成為互聯(lián)網(wǎng)信息高速網(wǎng)絡(luò)上所傳送數(shù)據(jù)的主要形式。多媒體信息包括圖像、音頻和視頻信息等，圖像是一種重要的表現(xiàn)形式，而且蘊(yùn)涵著豐富的情感信息。Flash動(dòng)畫(huà)是近些年來(lái)在網(wǎng)絡(luò)上流行和教育教學(xué)中普遍使用的多媒體形式，它包含著大量的動(dòng)畫(huà)畫(huà)面，畫(huà)面形式、內(nèi)容多種多樣，表達(dá)情感豐富，對(duì)Flash動(dòng)畫(huà)畫(huà)面的視覺(jué)特征與情感的研究就成為本文的研究課題。 本文針對(duì)Flash動(dòng)畫(huà)中的動(dòng)態(tài)畫(huà)面進(jìn)行截取作為研究圖像的樣本庫(kù)，提取每幅圖像的顏色和紋理低階視覺(jué)特征的相關(guān)參數(shù)，設(shè)計(jì)用16種情感形容詞（溫馨、歡快、激烈等）描述圖像表達(dá)的情感，用0-4五個(gè)等級(jí)定量圖像在某個(gè)情感上的表現(xiàn)，0表示無(wú)關(guān)，4表示充分表現(xiàn)，完成了Flash動(dòng)畫(huà)圖像樣本庫(kù)的建立、Flash動(dòng)畫(huà)圖像的情感描述和定量、提取每幅Flash動(dòng)畫(huà)圖像的顏色和紋理低階視覺(jué)特征工作，為基于圖像情感分類(lèi)和檢索進(jìn)一步研究打下了基礎(chǔ)。 主要工作如下： (1) Flash動(dòng)畫(huà)圖像樣本庫(kù)的建立。由于要研究的是Flash動(dòng)畫(huà)畫(huà)面，所以首先需要建立一個(gè)涵蓋種類(lèi)較多、數(shù)量較大、有一定代表性的Flash動(dòng)畫(huà)畫(huà)面的圖像樣本庫(kù)。Flash動(dòng)畫(huà)常分為MTV、動(dòng)畫(huà)、廣告、課件、賀卡、游戲等6大類(lèi)，其中常見(jiàn)的是MTV、動(dòng)畫(huà)、游戲等，，從已有的Flash動(dòng)畫(huà)庫(kù)中將Flash動(dòng)畫(huà)根據(jù)其表達(dá)的內(nèi)容和形式分為6大類(lèi)，然后根據(jù)每一類(lèi)Flash動(dòng)畫(huà)信息量的大小用Flash Decompiler Trillix軟件截取圖像，比如常見(jiàn)的MTV，反映信息量比較大，畫(huà)面內(nèi)容比較豐富，所以截取圖像的數(shù)量就多些。針對(duì)每一個(gè)Flash動(dòng)畫(huà)截取圖像的原則是采樣的時(shí)間間隔盡量短、圖像信息量相對(duì)豐富、避免類(lèi)似畫(huà)面的重復(fù)等，共截取6大類(lèi)共2737幅Flash動(dòng)畫(huà)畫(huà)面組成要研究的圖像樣本庫(kù)。 (2)提取每幅圖像的顏色和紋理低階視覺(jué)特征。圖像的低階視覺(jué)特征包括顏色、紋理、形狀等，F(xiàn)lash動(dòng)畫(huà)畫(huà)面色彩明顯，往往通過(guò)色彩的渲染表達(dá)情感，所以顏色特征的提取至關(guān)重要，同時(shí)對(duì)圖像的紋理特征也進(jìn)行了研究和提取。用C++和matlab編程實(shí)現(xiàn)HSV顏色空間顏色特征提取，顏色直方圖特征向量256維作為顏色特征，同時(shí)實(shí)現(xiàn)基于共生矩陣紋理特征提取，能量、熵、慣性矩、相關(guān)的均值和標(biāo)準(zhǔn)差作為最終8個(gè)參數(shù)表示紋理特征，制成低階視覺(jué)特征表格。 (3) Flash動(dòng)畫(huà)圖像的情感描述和定量。每一幅圖像都包含和表達(dá)不同的情感，有的情感表現(xiàn)的明顯，有些不明顯，為了較全面地描述一幅圖像的情感表現(xiàn)，采用16種情感形容詞,包括溫馨、恬靜、歡快、活潑、搞笑、夸張、幽默、有趣、凄涼、枯燥、沉悶、繁亂、虛幻、驚險(xiǎn)、恐怖、激烈，用0-4五個(gè)等級(jí)定量圖像在某個(gè)情感形容詞的程度，0表示無(wú)關(guān)，1表示略有表現(xiàn)，2表示一般表現(xiàn)，3表示表現(xiàn)明顯，4表示充分表現(xiàn)，通過(guò)自我評(píng)定和實(shí)驗(yàn)室人員幫助完成對(duì)2737幅圖像情感的量化，使得每幅圖像主要表達(dá)的情感較準(zhǔn)確，制成圖像情感分析表。
[Abstract]:With the rapid development of multimedia and network technology, multimedia information has gradually become the main form of data transmitted on the Internet information high-speed network. Multimedia information includes image, audio and video information. Image is an important form of expression and contains rich emotional information. Flash animation is a popular multimedia form in recent years and widely used in education and teaching. It contains a large number of animated pictures, picture forms, various content, rich expression of emotion, the study of visual features and emotions of Flash animation picture has become the research topic of this paper. In this paper, the dynamic images in Flash animation are intercepted as the sample library of the study images, the parameters of the low-order visual features of each image are extracted, and 16 kinds of emotional adjectives (warm, cheerful, cheerful) are designed. ) describe the emotion of image expression, use 0-4 grade quantitative image in a certain emotion performance, 0 denote irrelevant, 4 denote full performance, completed the establishment of Flash animation image sample database. The emotion description and quantification of Flash animation images, and the extraction of the low order visual features of each Flash animation image, lay a foundation for the further research of image emotion classification and retrieval. The main work is as follows: (1) Establishment of Flash animation image sample library. Because we want to study the Flash animation picture, we need to establish an image sample database covering a large number of Flash animation pictures. Flash animation is usually divided into MTV, animation, advertisement, courseware, greeting card, etc. There are 6 kinds of games, such as MTV, animation, game, etc. The Flash animation is divided into 6 categories according to its expression content and form from the existing Flash animation library. Then according to the size of each kind of Flash animation information, we use Flash Decompiler Trillix software to intercept the image, such as the common MTV, reflects the large amount of information, the picture content is relatively rich, so the number of captured images is more. In view of the principle of each Flash animation to capture images, the sampling interval is as short as possible, the amount of image information is relatively abundant, and the repetition of similar images is avoided. A total of 2737 Flash animation images of 6 categories are intercepted to form the image sample library to be studied. (2) extracting the low order visual features of each image. The low-order visual features of the image include color, texture, shape and so on. The color of the Flash animation screen is obvious and often expresses emotion through the color rendering, so the extraction of the color feature is very important. At the same time, the texture features of the image are also studied and extracted. C and matlab are used to realize the color feature extraction of HSV color space. The color histogram feature vector 256-dimension is used as the color feature. At the same time, based on co-occurrence matrix texture feature extraction, energy, entropy, moment of inertia, energy, entropy and inertia moment are realized. The associated mean and standard deviation are used as the final eight parameters to represent the texture feature, and the low order visual feature table is made. (3) emotional description and quantification of Flash animation image. Each image contains and expresses different emotions, some of which are obvious and some are not. In order to describe the emotional performance of an image more comprehensively, 16 emotional adjectives are used, including warmth, tranquility, joy and vivacity. Funny, exaggerated, humorous, funny, desolate, boring, dreary, messy, illusory, thrilling, scary, intense, with 0-4 levels of quantitative images on the degree of an emotional adjective, 0 for nothing, 1 for slight performance, 2 for general performance, 3 for obvious performance, 4 for full performance. Through self-assessment and laboratory personnel to help complete the quantification of 2737 images, the main emotions expressed in each image were more accurate. Make image emotion analysis table.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類(lèi)號(hào)】：TP391.41

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