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In recent years, much progress has been made in the area of video indexing and metadata extraction These efforts are aimed at transforming video into useful, searchable information Examples of video indexing techniques include keyframing, closed-caption text extraction, speech-to-text processing, speaker identification, face identification, on-screen text recognition, dropout detection, object (or logo) detection, and so on The Virage VideoLogger is the premier example of the commercial deployment of these technologies Each of these indexing approaches uses some form of algorithmic signal or text analysis to extract meaningful metadata, which are then time stamped to correlate the occurrence of the metadata item with the location within the video where that metadata occurred These indexing approaches can be thought of as observations derived from some form of signal analysis within a signal modality (for example, video pixels, audio samples, and text) They have significant value in terms of forming the basis for a searchable index that can be exploited in a variety of application areas (for example, Web publishing, media asset management, and video archives) to make the video accessible and useful in a fine-grained, nonlinear way



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Additionally, u = S TS is the traf c intensity, = u/N D is the server utilization, and E C (N D , u) is the Erlang-C function: E C (N D , u) = u N D /N D ! u N D /N D ! + (1 )

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For the fundamental problem of integrating iterative and noniterative computations into one ef cient graph model, the idea of a hierarchical graph (Sinnen and Sousa [175]) emerged, where a node of a higher level can represent an entire subgraph With the end of this chapter, the foundation for the discussion of task scheduling in the next chapter has been laid

(19.13)





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The next generation of video indexing technology will build on the observational modalities of present systems to provide higher-level semantic information and structure, resulting in a more useful and powerful video index The primary goal of current research is to automatically determine meaningful boundaries within the video, wherein the subject matter of the video is coherent This process is known as video segmentation and results in a higher-level structure of clips (defined by in- and out-points within a larger piece of video), thereby providing richer opportunities for search, retrieval, classification, and summarization of video content Currently, human beings mainly perform video segmentation in commercial applications by interacting with a video indexing system to mark the clip boundaries and assign categories and summaries in a manual process The next generation will largely automate this process, as well as produce additional value, with such features as summarization, topic identification, and proper noun identification Virage long ago anticipated the emergence of second-order analysis techniques and pioneered a framework capable of supporting the multimodal fusion approach to analysis Not only can media analysis plug-ins subscribe to raw signals (pixels, audio samples), but they also can access any metadata track generated by other plug-ins Moreover, groups of plug-ins can cooperate and exchange information with human beings, providing the framework for the next generation of video indexing

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Since the traf c intensity depends on the average waiting time, and the traf c intensity is needed to compute the average waiting time, equation (19.10) is in fact recursively de ned. Due to equation (19.13), equation (19.10) does not appear to be analytically solvable. Therefore, we apply numerical methods to solve for WC ( ) in computing the numerical results presented in Section 19.4. Now that we have obtained the waiting time for a START request, we can proceed to compute the average waiting time for dynamically-admitted client requests. Speci cally, we assume the waiting time for a START request is exponentially distributed with mean WC ( ). We classify client requests into two types. A Type-1 request is the rst request that arrives at the beginning of the admission cycle. Type-2 requests are the other requests that arrive after a Type-1 request. For example, request 1 in Figure 19.8 is a Type-1 request, and request 2 and 3 are Type-2 requests. We rst derive the average waiting time for Type-2 requests. Let W2 ( ) be the average waiting time for Type-2 requests which can be shown to be (please refer to the Appendix): W2 ( ) = WC ( ) 1 1 + (TR 2 )/2WC ( ) 1 e

37 EXERCISES 31 Section 31 reviews basic graph concepts for undirected and directed graphs Consider the following directed graph G = (V, E) consisting of 6 vertices and 9 edges:

Virage s VideoLogger has a rich, open, and extensible architecture supported by a robust software development kit (SDK) One of the central interfaces in this architecture is the media analysis framework

(T R 2 ) WC ( )

(TR 2 ) (TR 2 ) e WC ( ) WC ( )

(a) Which vertex has the highest degree (b) What is the average in-degree of the vertices What is the average outdegree

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