By C.-C. Jay Kuo, Chen Chen, Yuzhuo Ren
This ebook deals an outline of conventional tremendous visible info research techniques and gives cutting-edge suggestions for numerous scene comprehension difficulties, indoor/outdoor class, outdoors scene category, and outdoors scene format estimation. it truly is illustrated with various usual and artificial colour photographs, and large statistical research is supplied to assist readers visualize immense visible information distribution and the linked difficulties. even though there was a little research on immense visible information research, little paintings has been released on titanic photo info distribution research utilizing the fashionable statistical procedure defined during this booklet. via providing a whole method on enormous visible facts research with 3 illustrative scene comprehension difficulties, it presents a favourite framework that may be utilized to different enormous visible info research projects.
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Additional info for Big Visual Data Analysis: Scene Classification and Geometric Labeling
In this section, we propose a novel method to group data according to the capabilities of different experts in the contexts of indoor/outdoor classification and vanishing point detection. We select 18 images from the SUN database and show them in Fig. 24. They will be used as examples to facilitate our discussion below. Without loss of generality, we use expert KPK  as an illustrative example for indoor/outdoor scene classification. kpk For the jth image sample, denoted by Ij , KPK can generate a soft decision score, dj , for it using its sample-to-boundary distance normalized to the range of [0, 1], where 0 and 1 indicate the indoor and outdoor scenes with complete confidence, respectively.
In the implementation, the SP method constructed a 15-dimensional feature vector obtained from three scales for each block and used the Mahalanobis norm to measure the distance between these vectors. DCT. The 2-D Discrete Cosine Transform (DCT) is used in SP as frequency features. In the implementations, sub-blocks of size 8 × 8 are used in the DCT computation, and results are averaged over all sub-blocks in each block. Since periodic textures will show a regular pattern of peaks (fundamental and harmonic frequencies) after the DCT, coefficients of all corresponding frequencies can be averaged to yield one single coefficient.
1 empirically. This idea is illustrated in Fig. 19. Then, we quantize the hue values into 16 bins. Each hue bin is further divided into 5 saturation bins. 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.