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Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
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Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
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2. 統計抽樣
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1 1 2
4 5 6 7 8 3 2 1 0 1 2 3 4 6 8 3 4 1 2 1 0 3.25 7 2 2 5 8 3 4 Max{1,1,5,6}=6 Output =2*2
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1 2 3
4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 高 寬 輸入圖 7 11 輸出圖 2 2 視窗大小 7/2=3.5取4 11/2=5.5 取 6 移動大小 7/2=3.5取3 11/2=5.5 取 5 紅色 高1寬1~高4 寬 6 綠色 高1寬6~高4 寬 11 黃色 高4寬1~高7寬 6 藍色 高4寬6~高7寬 11
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A Gaussian Pyramid
(SIFT) B Laplacian Pyramid A Spatial Pyramid(SPM) B Pyramid Histogram of Oriented Gradients(PHOG用於VGG) C Pyramid Histogram of Visual Words
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亞像素像素超像素 SLIC
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抽樣空間分層Pooling / / / /
/ / / / Spatial bins輸入圖像
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1 1 4
4 4 4 5 5 1 1 4 4 4 4 5 5 3 3 9 9 2 2 3 3 3 3 9 9 2 2 3 3 8 8 1 1 6 6 0 0 8 8 1 1 6 6 0 0 0 0 3 3 2 2 1 1 0 0 3 3 2 2 1 1 1 4 4 5 3 9 2 3 8 1 6 0 0 3 2 1 9 5 8 6 9 9 9 5 8 6 1 4 4 5 3 9 2 3 8 1 6 0 0 3 2 1 8*8 input
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1 4 4
5 3 9 2 3 8 1 6 0 0 3 2 1 9 5 8 6 9 9 9 5 8 6 1 4 4 5 3 9 2 3 8 1 6 0 0 3 2 1 1 4 4 5 3 9 2 3 8 1 6 0 0 3 2 1 4*4 input
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