By C.-C. Jay Kuo, Chen Chen, Yuzhuo Ren
This e-book deals an summary of conventional immense visible facts research ways and gives cutting-edge options for numerous scene comprehension difficulties, indoor/outdoor type, outside scene class, and outdoors scene structure estimation. it truly is illustrated with quite a few traditional and artificial colour photographs, and huge statistical research is equipped to assist readers visualize huge visible facts distribution and the linked difficulties. even though there was a little analysis on large visible facts research, little paintings has been released on monstrous photo info distribution research utilizing the fashionable statistical strategy defined during this booklet. through proposing a whole technique on substantial visible information research with 3 illustrative scene comprehension difficulties, it presents a ordinary framework that may be utilized to different titanic visible facts research initiatives.
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Additional info for Big Visual Data Analysis: Scene Classification and Geometric Labeling
To calculate the distance between two histograms, the histogram intersection norm  was used. It measures the amount of overlap between the corresponding buckets in two histograms h1 and h2 as given by N dist(h , h ) = 1 (hi1 − min(hi1 , hi2 )). 2 %. MSAR texture feature. The multi-resolution simultaneous autoregressive model (MSAR)  was used as the texture feature in SP. It was one of popular texture features in early days when benchmarked on the Brodatz album . The MSAR offers a good linear predictor of a pixel based on its non-causal neighborhood.
129]. 7) They are applied to both rows and columns of an image separately with downsampling to yield a 2-D separable wavelet transform as shown in Fig. 8, where c2 , c3 , c4 , c5 , c6 , c7 and c8 represent sub-band coefficients. 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, .
The LUV color space is adopted by VFJ to evaluate the color property of an image. Typically, the RGB color space is first transformed to the XYZ color space and, then, the XYZ color space is transformed to the LUV space. According to theoretical analysis and experimental evidences in , LUV moments yield better results in image retrieval than moments in other spaces. The VFJ expert uses the first-order and second-order moments of the three channels of the LUV space. Since each image is divided into 100 blocks and there are 6 color moment features in each block, the feature vector of an image has a dimension of 600.