臺灣博碩士論文加值系統

在本篇論文中，我們提出了一種新的有效影像特徵萃取方式，用來描述紋理及色彩，並以此作為影像擷取系統的基礎。此描述子稱為是四方向特徵趨勢描述子（Four-directional Feature-trend descriptor），加入碎形分析(fractal analysis)，用來描述影像的色彩及紋理特徵，此描述子是建構在有方向性的顏色變化所產生的紋理。
首先將RGB色彩空間轉換至HSV色彩空間，取出HSV中的色調分量(Hue component)，接著將原始影像轉為灰階影像後，進行碎形維度分析，對色調分量影像以及進行碎形維度分析後的灰階影像量化，再提取此兩種影像的顏色及紋理資訊。四方向特徵趨勢建立於8D-RL描述子的基礎，我們重新定義四個方向的顏色紋理，從影像最上列的每一個點出發，往四個方向前進，計算鄰居點與該點是否有相同的色調及灰階量化值，若相同則繼續前進，直到與出發點的值不同為止，然後紀錄此區間相同量化值的個數，重複以上動作直到該方向全部的點都計算過為止，將該方向所有區間具有相同某一量化值的個數取平方和後當在該方向對此一量化值的特徵，此描述子是對所有色調及灰階強度之量化值在四個方向所整合而成的影像特徵向量。
我們使用Corel-1000影像資料集進行實驗，此資料庫有一千張自然影像，分為十個類別，每個類別各有一百張影像，每一個類別我們隨機選取了五十張影像，共五百張query images進行實驗。實驗結果顯示我們所提出的影像特徵擷取方法很有效地描述影像的顏色和紋理特徵資訊，與其它人所提出的方法相比，具有更高的精確度（Precision）與召回度（Recall）。

In this thesis, we propose a new image feature extraction and representation method to extract and describe the texture and color features of images as a basis for image retrieval systems. First, a color image is converted from RGB color space to HSV color space before extracting the hue component of HSV. The original image is transformed into a gray-scale image from which the local singularity feature of the image is analyzed based on fractal theory. The hue component and local singularity features of the image are quantized and represented by a method called four-direction feature-trend descriptor.
We use the images in Corel-1000 database to conduct our experiments. Corel-1000 database has a total number of 1000 images classified into 10 categories. We randomly select fifty images from each category as the query images for the image retrieval system. Experimental results show that our four-direction feature trend feature descriptor can combine color and texture features very effectively and has better performance in terms of precision and recall as compared to the performance of other methods.

目錄

第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 論文架構 3
第二章相關研究探討 4
2.1 色彩及紋理特徵 4
2.1.1 色彩特徵 5
2.1.2 紋理特徵 5
2.2 HSV色彩空間 6
2.3 碎形維度的分析 8
2.4基於微結構描述子的影像特徵萃取法 11
2.4.1 色彩量化 11
2.4.2 邊界點方向偵測 11
2.4.3 微結構描述子(Micro-structure descriptor, MSD) 12
2.5修正後的色差直方圖特徵法 15
2.5.1 色彩量化 15
2.5.2 邊緣取向量化與特徵的合併 16
2.6基於8D-RL描述子的影像擷取系統 19
2.6.1 色調分量影像及灰階影像之量化 19
2.6.2 基於8D-RL之特徵萃取法 20
2.6.3 基於8D-RL之特徵表示法 21
第三章新的特徵萃取與表示法 22
3.1 色調分量影像量化 22
3.2 碎形維度的量化萃取 23
3.3 特徵萃取法 26
3.4 特徵表示法 29
第四章 實驗結果與分析 33
4.1 影像資料庫 33
4.2 相似性比較排序 34
4.3 效能評估方式 35
4.4 結果與分析 37
第五章結論與未來展望 48
參考文獻 49

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