基于顏色的圖像檢索在藝術教育中的應用
發(fā)布時間：2019-06-26 21:28
【摘要】：當今，素質教育讓藝術教育在教育中的地位越來越重要，藝術教育尤其是美術教育，需要儲備豐富的圖像資源才能讓學生的思維空間變得更為寬闊。計算機技術和多媒體技術高度發(fā)展的今天，互聯(lián)網為我們提供了豐富的資源，，我們自己搜集的資源也可以放在校園網上與人共享。網絡已經成為人們獲取信息的重要途徑，網上信息的檢索變得尤為重要。 目前圖像檢索方法主要有兩類�；陉P鍵詞的圖像檢索和基于內容的圖像檢索�；趦热莸膱D像檢索(CBIR)是當前圖像檢索技術的研究熱點之一，它通過對圖像的內容特征(如圖像的顏色、形狀、紋理等)進行分析和提取，建立圖像特征索引庫，然后根據圖像內容特征的相似性檢索圖像，而使檢索結果在視覺特征上具有更好的一致性。在基于內容的圖像檢索中，顏色是最直觀、最明顯的視覺特征�；陬伾膱D像檢索通過提取圖像的顏色特征，利用顏色特征間的相似度實現圖像的檢索。 顏色特征的提取，首先需要選取合理的顏色模型表示顏色。RGB模型較常用，但不符合人們對顏色相似性的主觀判斷，故在圖像顏色分析中常采用HSV模型。然后，在對顏色空間進行合理的量化后，利用顏色直方圖法分析圖像的顏色特征。常用的顏色特征分析方法有統(tǒng)計直方圖法，累加直方圖法，直方圖相交法，比例直方圖法，距離法，參考顏色表法，聚類算法和HSI中心矩法等。通過顏色聚類提取出圖像的主色調，存入圖像的顏色特征索引庫。 論文以幾個著名的基于內容的圖像檢索實驗系統(tǒng)為例，闡述了基于顏色特征的圖像檢索方法，并探討了基于顏色的圖像檢索在美術教學中應用的問題。通過教學實例，體會到圖像檢索技術給美術教學帶來的積極作用，對培養(yǎng)我們的美術學生在繪畫方面的顏色選取能力、顏色的合理搭配能力將會起到積極的效果，更能體現信息技術教育與藝術教育整合的優(yōu)越性，有利于促進藝術教育的發(fā)展。
[Abstract]:Nowadays, quality education makes art education more and more important in education. Art education, especially art education, needs to reserve rich image resources in order to make students' thinking space wider. With the high development of computer technology and multimedia technology, the Internet provides us with rich resources, and the resources we collect can also be shared with people on the campus network. The network has become an important way for people to obtain information, and the retrieval of online information has become particularly important. At present, there are two main image retrieval methods. Keyword-based image retrieval and content-based image retrieval. Content-based image retrieval (CBIR) is one of the research hotspots in current image retrieval technology. by analyzing and extracting the content features (such as image color, shape, texture, etc.), the image feature index library is established, and then the image is searched according to the similarity of image content features, so that the retrieval results are more consistent in visual features. In content-based image retrieval, color is the most intuitive and obvious visual feature. The color-based image retrieval realizes the image retrieval by extracting the color features of the image and using the similarity between the color features. In order to extract color features, it is necessary to select a reasonable color model to represent color. RGB model is more commonly used, but it does not accord with people's subjective judgment of color similarity, so HSV model is often used in image color analysis. Then, after the color space is reasonably quantified, the color features of the image are analyzed by color histogram method. The commonly used color feature analysis methods are statistical histogram method, cumulative histogram method, histogram intersection method, proportional histogram method, distance method, reference color table method, clustering algorithm and HSI central moment method. The main tone of the image is extracted by color clustering and stored in the color feature index library of the image. Taking several famous content-based image retrieval experimental systems as examples, this paper expounds the image retrieval method based on color features, and discusses the application of color-based image retrieval in art teaching. Through teaching examples, we can realize the positive effect of image retrieval technology on art teaching, which will play a positive effect on cultivating the color selection ability and color reasonable collocation ability of our art students in painting, and can better reflect the advantages of the integration of information technology education and art education, and is conducive to promoting the development of art education.
【學位授予單位】：山東師范大學
【學位級別】：碩士
【學位授予年份】：2005
【分類號】：G354;J0-4
本文編號：2506507
[Abstract]:Nowadays, quality education makes art education more and more important in education. Art education, especially art education, needs to reserve rich image resources in order to make students' thinking space wider. With the high development of computer technology and multimedia technology, the Internet provides us with rich resources, and the resources we collect can also be shared with people on the campus network. The network has become an important way for people to obtain information, and the retrieval of online information has become particularly important. At present, there are two main image retrieval methods. Keyword-based image retrieval and content-based image retrieval. Content-based image retrieval (CBIR) is one of the research hotspots in current image retrieval technology. by analyzing and extracting the content features (such as image color, shape, texture, etc.), the image feature index library is established, and then the image is searched according to the similarity of image content features, so that the retrieval results are more consistent in visual features. In content-based image retrieval, color is the most intuitive and obvious visual feature. The color-based image retrieval realizes the image retrieval by extracting the color features of the image and using the similarity between the color features. In order to extract color features, it is necessary to select a reasonable color model to represent color. RGB model is more commonly used, but it does not accord with people's subjective judgment of color similarity, so HSV model is often used in image color analysis. Then, after the color space is reasonably quantified, the color features of the image are analyzed by color histogram method. The commonly used color feature analysis methods are statistical histogram method, cumulative histogram method, histogram intersection method, proportional histogram method, distance method, reference color table method, clustering algorithm and HSI central moment method. The main tone of the image is extracted by color clustering and stored in the color feature index library of the image. Taking several famous content-based image retrieval experimental systems as examples, this paper expounds the image retrieval method based on color features, and discusses the application of color-based image retrieval in art teaching. Through teaching examples, we can realize the positive effect of image retrieval technology on art teaching, which will play a positive effect on cultivating the color selection ability and color reasonable collocation ability of our art students in painting, and can better reflect the advantages of the integration of information technology education and art education, and is conducive to promoting the development of art education.
【學位授予單位】：山東師范大學
【學位級別】：碩士
【學位授予年份】：2005
【分類號】：G354;J0-4
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