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Download Big Visual Data Analysis: Scene Classification and Geometric by C.-C. Jay Kuo, Chen Chen, Yuzhuo Ren PDF

By C.-C. Jay Kuo, Chen Chen, Yuzhuo Ren

This booklet deals an summary of conventional large visible info research ways and offers state of the art ideas for numerous scene comprehension difficulties, indoor/outdoor class, outside scene category, and outside scene structure estimation. it's illustrated with various average and artificial colour photos, and wide statistical research is equipped to aid readers visualize large visible information distribution and the linked difficulties. even if there was a little analysis on substantial visible info research, little paintings has been released on great photograph facts distribution research utilizing the fashionable statistical process defined during this booklet. by way of proposing an entire technique on monstrous visible facts research with 3 illustrative scene comprehension difficulties, it offers a ordinary framework that may be utilized to different titanic visible info research initiatives.

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Additional info for Big Visual Data Analysis: Scene Classification and Geometric Labeling

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Similarly, the top horizontal block carries the valuable sky information for outdoor images 7 – 9 . Misclassified Images. Outdoor images 1 – 3 and indoor images 7 – 9 are misclassified although their scores fall in the confident regions. They are called outliers. Outdoor images 1 – 3 all have dark colors and clear edge structures over the entire image, which misleads KPK. The blue top part of indoor image 7 is also misleading. Indoor image 8 is difficult since its wall contains the outdoor view and painting.

Color Orientation Histogram (COH) and Edge Orientation Histogram (EOH). The COH is computed using the HSV color model [12] as illustrated in Fig. 14. The procedure to transform from the RGB color space to the HSV color space can be written as: The values of R, G, B three color channels are all normalized to [0,1]. Cmax = max(R, G, B) Cmin = min(R, G, B) Δ = Cmax − Cmin ⎧ mod 6) Cmax = R ⎨60 × ( G−B Δ H= 60 × ( B−R + 2) Cmax = G Δ ⎩ 60 × ( R−G + 4) Cmax = B Δ S= 0 Δ=0 Δ=0 Δ Cmax V = Cmax Fig. 12) 38 3 Indoor/Outdoor Classification with Multiple Experts The hue value represents a color tone with an angle in the HSV color space.

Support Vector Machine (SVM) and Decision Pooling. The support vector machine (SVM) is a supervised learning tool that analyzes data for classification and regression. The original SVM algorithm was originally proposed by Vapnik, and its current form (soft margin) was developed by Cortes and Vapnik [6]. It aims at maximum margin classification and reduces the error caused by traditional linear or nonlinear classification boundaries. It was first adopted by SSL for the indoor/outdoor classification problem.

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