Cybercultures: Critical Concepts in Media and Cultural by David Bell

By David Bell

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But when D > N, the basic methodology which was used in the classical situation is not similarly applicable. 2. Imbalance of training classes. The target class in the training pool is typically outnumbered by the non-target classes (N - >> N+). For instance, in a k-class classification problem where each class has about the same number of training instances, the target class is outnumbered by the non-target classes by a ratio of k-1:1. The class boundary of imbalanced training classes tends to skew toward the target class when k is large.

Once the classifier is trained, SVMActive returns the top-k most relevant images. These are the k images farthest from the hyperplane on the query concept side. In short, we can use MEGA to find initial relevant images, and then switch to SVMActive for refining the binary classifier and ranking returned images. This method can effectively learn a query concept when the complexity of a concept is not too high. We will define concept complexity later in the chapter, and present remedies for searching concepts of high complexity.

2 The use of a no-reference metric is particularly interesting here because semantic segmentation does not require a reference video either. Copyright © 2006, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 14 Cavallaro & Winkler Figure 10. Block diagram of no-reference quality metric Jerkiness Metric Video Blockiness Metric Artifact weighting Quality rating Blur Metric The blockiness metric looks for typical block patterns in a frame.

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