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

This publication deals an summary of conventional massive visible info research techniques and gives state of the art options for a number of scene comprehension difficulties, indoor/outdoor class, outdoors scene category, and outdoors scene format estimation. it's illustrated with quite a few traditional and artificial colour photos, and broad statistical research is supplied to assist readers visualize large visible information distribution and the linked difficulties. even though there was a little research on substantial visible facts research, little paintings has been released on colossal photograph info distribution research utilizing the trendy statistical method defined during this ebook. by means of featuring a whole method on tremendous visible info research with 3 illustrative scene comprehension difficulties, it presents a everyday framework that may be utilized to different substantial visible facts research projects.

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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 [40], 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.

4 Diversity Gain of Experts Via Decisions Stacking 53 Fig. 34 %, which is better than the performance of the nine-expert system. This is attributed to the overfitting problem in machine learning. When the number of experts becomes larger, their weights (and, thus, performance) are more sensitive to the size of the training data. It is also worthwhile to point out that our discussion in above applies to the selected 5,000 images, and the performance behavior may change with respect to a different dataset.

Without loss of generality, we choose KPK [19] and PY [29] as two experts for indoor/outdoor classification. The soft decision scores of expert PY, denoted by d py , for the same 5,000 samples (as plotted in Fig. 25) are plotted along the vertical axis in Fig. 26. The jth sample image represented by a red circle (indoor) or a green cross kpk py (outdoor) has two soft scores, denoted by (dj , dj ), which defines the KPK-PY soft decision map. With different combinations of soft decisions from two experts, we can divide the 2-D decision space into 9 regions as shown in Fig.

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