By C.-C. Jay Kuo, Chen Chen, Yuzhuo Ren
This booklet bargains an summary of conventional gigantic visible information research ways and offers cutting-edge strategies for a number of scene comprehension difficulties, indoor/outdoor category, outside scene class, and outdoors scene structure estimation. it really is illustrated with a number of normal and artificial colour photographs, and huge statistical research is supplied to assist readers visualize enormous visible info distribution and the linked difficulties. even supposing there was a little research on massive visible info research, little paintings has been released on substantial photo information distribution research utilizing the fashionable statistical procedure defined during this booklet. by means of proposing an entire technique on huge visible info research with 3 illustrative scene comprehension difficulties, it offers a usual framework that may be utilized to different colossal visible information research initiatives.
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Additional info for Big Visual Data Analysis: Scene Classification and Geometric Labeling
The low-frequency coefficient c5 is replaced by the Laplacian filtered one  since the texture has little low-frequency component. Then, texture features can be obtained by computing the energy function of the following seven sub-bands: ek = 1 Mk Nk Mk Nk |ck (i, j)|2 , k = 2, 3, . . 8) i=1 j=1 where Mk and Nk are dimensions of subband k. SSL uses wavelets to increase computational efficiency and reduce the dimension of the texture feature vector. In contrast with the 15-D MSAR features used in SP, its texture dimension is only seven.
5 An overview of the VFJ indoor/outdoor scene classification system (or expert) 30 3 Indoor/Outdoor Classification with Multiple Experts which are class-conditionally independent. 4) i=1 where Ω is the set of classes. For the case of indoor/outdoor scene classification, there are two classes. To infer the proper class type based on the Maximum A Posteriori (MAP) criterion, we have wˆ = arg max p(w|y) = arg max f (y|w)p(w). 5) To estimate class-conditional densities f (y|w), VFJ adopts the vector quantization technique  by leveraging an open source package called LVQ_PAK .
Consequently, it leads to a 80-bin hue-saturation histogram, which is used as the feature vector for training and testing. The HSH expert exploits the global hue-saturation distribution for indoor/outdoor classification. It is relatively simple since there is no image partition and decision fusion in the system. It will serve as a supplement to other indoor/outdoor classification experts. Fig. 19 The HSH expert selects a specific region in the HSV space for the hue-saturation histogram computation.