Digital Multimedia Perception And Design by Georghita Ghinea, Sherry Y. Chen

By Georghita Ghinea, Sherry Y. Chen

Electronic Multimedia notion and layout offers a well-rounded synopsis of the state of the art applied sciences in perceptual-based multimedia layout. it's the basically such booklet out there, uniting state-of-the-art examine through the various best humans within the box and filling a niche within the current multimedia layout literature. electronic Multimedia notion and layout highlights how perceptual standards could be integrated within the layout, improvement and transmission of multimedia, offering readers with a wealthy physique of study and paintings performed during this fascinating zone. This booklet aids all these drawn to development real end-to-end multimedia details structures, and comes in handy to researchers, multimedia designers, and content material and infrastructure services.

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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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