不同影像尺寸與不同特徵表達對影像辨識之影響

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鍾穎慧(Yong-Hui Chung) 查詢紙本館藏

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論文名稱

不同影像尺寸與不同特徵表達對影像辨識之影響
(Object Recognition with Different Image Resolution and Different Feature Representation)

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摘要(中)

隨著網路的蓬勃發展，影像資料的數量也日漸增加，有鑑於人工直接標註影像過於費時，自動物件辨識及影像註解議題應運而生。過去研究著重於如何在龐大的影像資料庫中有效率且準確的將影像自動命名。然而，隨著影像數量日漸增多，利用原尺寸影像進行標註，會降低演算法效能且占據大量儲存空間，此外，不同特徵表達方式也可能影響影像註解之準確率。因此本研究將分別以兩種構面探討其對影像註解之正確率影響。
實驗結果顯示，不同的特徵表達方式確實會影響影像註解之準確率，但影像解析度對於影像註解準確率的影響程度卻不高，且不同特徵表達方式受影像解析度的影響程度不同。
本研究使用 Corel、PASCAL 2008、Corel 5000 三種不同資料集，選擇影像內插法中最廣為運用的雙立方內插法(Bicubic Interpolation)將影像重新取樣(分為 256x256、128x128、64x64、32x32、16x16)，特徵表達方式則分為區域特徵表達(Local Feature)、袋字模型(Bag-of-Words)特徵表達兩種。

摘要(英)

With the advent of the Internet and an increase in web images, manual image annotation becomes a difficult task and more time-consuming than automatic image annotation. Most research proposed algorithms for matching the keywords and the images accurately. However, those methods annotated images in original resolution, and it might cost more time and storage. In addition, different feature representation approach can cause various performance of annotation .We aimed to annotate images with different resolution and different feature representation approach and discussed the effect of these two factors.
We chose Corel, PASCAL VOC2008 and Corel 5000 to be our experiment data sets, and selected Bicubic Interpolation to scale these data sets into 256x256 resolution, 128x128 resolution, 64x64 resolution, 32x32 resolution and 16x16 resolution. Furthermore, local feature representation and Bag-of-Words feature representation were used in our experiment. In annotation step, we used support vector machine and K nearest neighbor algorithms.
Finally, the experimental results indicated that the accuracy of annotation didn’t decrease but the time of annotation was reduced rapidly when the image resolution was diminished. Besides, we also compared two feature representation approaches, the performance of local feature representation was better than Bag-of-Words feature representation, especially in support vector machine. Meanwhile, in different resolution, the performance of Bag-of-Words feature representation was more stable than local feature representation.

關鍵字(中)

★ 影像特徵表達
★ 影像註解
★ 影像解析度
★ 物件辨識

關鍵字(英)

★ object recognition
★ feature representation
★ image annotation
★ image resolution

論文目次

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
第二章文獻探討 4
2.1. 影像註解 4
2.2. 特徵擷取及表達 5
2.2.1. 全域特徵及區域特徵 5
2.2.2. 袋字模型(Bag-of-Words) 7
2.3. 影像縮放技術 11
第三章實驗設計 12
3.1. 資料集 14
3.1.1 Corel資料集 14
3.1.3 PASCAL VOC2008 資料集 15
3.1.4 Corel 5000資料集 15
3.2. 第一階段實驗：不同影像尺寸對影像註解正確率之影響 16
3.2.1. 影像縮放(Image scaling) 16
3.2.2. 影像切割(Image segmentation) 17
3.2.3. 特徵萃取及描述 (Feature extraction and representation) 18
3.3. 第二階段實驗：不同特徵表達方式對影像註解正確率之影響 20
3.3.1. 影像縮放(Image Scaling) 20
3.3.2. 區域特徵表達(Local Feature representation) 20
3.3.3. 袋字模型特徵表達(Bag-of-Words Feature representation) 20
3.3.4. 影像註解分類器(Classifier) 24
3.3.5. 衡量方法 24
第四章實驗結果與討論 25
4.1. 第一階段實驗：不同影像尺寸對影像註解正確率之影響 25
4.1.1. Corel 190資料集 25
4.1.2. PASCAL VOC2008 資料集 28
4.2. 第二階段實驗：不同特徵表達方式對影像註解正確率之影響 31
4.2.1. Corel 5000資料集 31
4.2.2. PASCAL VOC2008資料集 32
第五章結論與未來研究方向 34
5.1. 小結 34
5.2. 未來研究方向 35
參考文獻 37
附錄一 44
附錄二 46

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指導教授

蔡志豐(Chih-Fong Tsai)

審核日期

2011-7-26

推文