By C.-C. Jay Kuo, Chen Chen, Yuzhuo Ren
This publication deals an outline of conventional mammoth visible facts research methods and gives cutting-edge recommendations for a number of scene comprehension difficulties, indoor/outdoor type, outdoors scene category, and open air scene structure estimation. it truly is illustrated with various typical and artificial colour pictures, and huge statistical research is equipped to aid readers visualize vast visible facts distribution and the linked difficulties. even though there was a little research on immense visible info research, little paintings has been released on large snapshot info distribution research utilizing the fashionable statistical procedure defined during this ebook. via offering a whole technique on substantial visible facts research with 3 illustrative scene comprehension difficulties, it presents a universal framework that may be utilized to different giant visible info research initiatives.
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Extra info for Big Visual Data Analysis: Scene Classification and Geometric Labeling
This is one of the main focuses of our research in this chapter. To address the large-scale indoor/outdoor scene classification problem, we propose an Expert Decision Fusion (EDF) system that consists of two key ideas—data grouping and decision stacking. In contrast with prior art, the proposed EDF system is less concerned with the search of new features but on a meaningful way to partition the dataset and organize basic indoor/outdoor classifiers in an effective way to lead to a more accurate and robust classification system.
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. Indoor image 9 can be even challenging to human being since one may make a different decision depending on the existence of the ceiling and the wall. For outlying images, low-level features mislead KPK to draw a confident yet wrong conclusion.
There are some red circles and green crosses in S2 , which are difficult to set apart using soft KPK decision scores. 9. Based on the above discussion, the criteria of a good expert can be concluded as follows. 1. It has a larger ratio of correct versus incorrect decision samples in S1 and S3 . 2. It has a smaller percentage of samples in S2 . Fig. 54 48 3 Indoor/Outdoor Classification with Multiple Experts We will discuss ways to achieve these two goals by inviting the second expert to join the decision-making process in Sect.
Big Visual Data Analysis: Scene Classification and Geometric Labeling by C.-C. Jay Kuo, Chen Chen, Yuzhuo Ren