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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题名:	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
作者:	鍾穎慧;Yong-Hui Chung
贡献者:	資訊管理研究所
关键词:	影像特徵表達;影像註解;影像解析度;物件辨識;object recognition;feature representation;image annotation;image resolution
日期:	2011-07-26
上传时间:	2012-01-05 15:12:44 (UTC+8)
摘要:	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。 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.
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