Chapter 3: Statistical Models of Video Structure and Semantics
Table 3.1: Classification accuracy of BmoViES ( 1998 IEEE).
Chapter 4: Flavor: A Language for Media Representation
Table 4.1: Compression effectiveness (CE ) for different XML compressors. CE = Compressed XML file / Original (bitstream) file.
Chapter 5: Integrating Domain Knowledge and Visual Evidence to Support Highlight Detection in Sports Videos
Table 5.1: Results for the classification of keyframes in terms of playfield, player and audience classes.
Table 5.2: Sports type identification results. The evaluation set in the first experiment comprised also player and audience scenes. The second experiment was carried out on playfield scenes only.
Table 5.3: Highlight classification results. The Corner/Penalty kick class comprises also free kicks near the goal box zone
Table 5.4: The Other class contains actions that were not modeled. The Corner/Penalty kick class comprises also free kicks near the goal box zone.
Chapter 7: Temporal Segmentation of Video Data
Table 7.1: Output values of the MLP during the training phase
Table 7.2: Characteristics of dataset
Table 7.3: Performance using the b2b metric
Table 7.4: Performance using the chi-square metric
Table 7.5: Performance using the histogram correlation metric.
Table 7.6: Factors intervening in transition detection (technique T2)
Table 7.7: Range of parameters used for experiments
Table 7.8: Optimal result using bin-to-bin metric
Table 7.9: Optimal result using chi-squares metric
Table 7.10: Optimal result using correlation metric
Table 7.11: Performance of MLPs of different architecture
Table 7.12: Performance comparison among the described techniques
Chapter 8: A Temporal Multi-Resolution Approach to Video Shot Segmentation
Table 8.1: Transition Boundary for Echosystem.mpg
Table 8.2: Videos for Evaluation
Table 8.3: Transition Type Distribution
Table 8.4: Result Using Twin-Comparison
Table 8.5: Result on TMRA using 64-bin Histogram Without Elimination Phase
Table 8.6: Result on TMRA using DC Histogram Without Elimination Phase
Table 8.7: Result on TMRA using DC64 Without Elimination Phase
Table 8.8: Result on TMRA Integrated with Elimination Phase Using DC64 and MA64
Chapter 10: Audio and Visual Content Summarization of a Video Program
Table 10.1: Particulars of the Evaluation Database.
Table 10.2: Statistics of the Manual Summarization Results.
Table 10.3: Evaluation Results.
Table 10.4: Experimental Evaluation.
Chapter 15: Video Shot Detection using Color Anglogram and Latent Semantic Indexing: From Contents to Semantics
Table 15.1: Evaluations of Experimental Results
Chapter 22: Organizational Principles of Video Data
Chapter 27: Indexing Video Archives: Analyzing, Organizing, and Searching Video Information
Table 27.1: Number of distinctive NNs.
Table 27.2: Categories of the results.
Chapter 28: Efficient Video Similarity Measurement using Video Signatures
Table 28.1: Comparison between using uniform random and Corel image SV's. The second through fifth columns are the results of using uniform random SV's and the rest are the Corel image SV's. Each row contains the results of a specific test video at IVS levels 0.8, 0.6, 0.4 and 0.2. The last two rows are the averages and standard deviations over all the test sequences.
Table 28.2: Comparison between VSSb and VSSr under different level of perturbation. The table follows the same format as in Table 28.1. The perturbation levels ε tested are 0.2, 0.4. 0.8, 1.2 and 1.6.
Table 28.3: Statistics of collected web video sequences
Chapter 30: Small Sample Learning Issues for Interactive Video Retrieval
Table 30.1: Averaged hit rate in top 100 for 500 rounds of testing.
Chapter 31: Cost Effective and Scalable Video Streaming Techniques
Table 31.1: Backward and forward interactions.
Chapter 32: Design and Development of a Scalable End-to-End Streaming Architecture
Table 32.1: A comparison of continuous-media servers. Note that Yima is a prototype system and does not achieve the refinement of the commercial solutions. However, we use it to demonstrate several advanced concepts.
Table 32.2: List of terms used repeatedly in this section and their respective definitions.
Chapter 34: Video Streaming: Concepts, Algorithms, and Systems
Table 34.1: Current and emerging video compression standards.
Table 34.2: Adapting error control based on differing importance of video data: unequal error protection and unequal (prioritized) retransmission based on coded frame type.
Chapter 35: Continuous Display of Video Objects using Heterogeneous Disk Subsystems
Table 35.1: Three different Seagate disk models and their zone characteristics.
Table 35.2: List of terms used repeatedly in this chapter.
Table 35.3: Three imaginary disk models and their zone characteristics.
Chapter 37: Server-Based Service Aggregation Schemes for Interactive Video-on-Demand
Table 37.1: Taxonomy of Service Aggregation Schemes in VoD
Table 37.2: Simulation Parameters
Table 37.3: Arrival and Interaction Patterns
Table 37.4: Comparison between Variants of RSMA (λarr = 0.1, λint = 0, λq = 0)
Table 37.5: Comparison between Variants of RSMA (λarr =0.1, λint = 0.01, λq = 0.001)
Chapter 38: Challenges in Distributed Video Management and Delivery
Table 38.1: Bitrate of differentially encoded streams and storage savings with respect to independent coding of the streams for the CIF sequence Football.
Table 38.2: Mapping data to data classes based on its position in the cache. The position is determined by the data's recall value.
Table 38.3: Mapping data class numbers to best allowed compression quality.
Table 38.4: Comparison of penalty in distortion for three users for MDFEC designed for the worst-case channel, and for MDFEC designed for the minimax cost.
Chapter 39: Video Compression: State of the Art and New Trends
Table 39.1: Tools supported in JVT/H.26L
Table 39.2: Average bitrate savings of JVT/H.26L
Table 39.3: Image and video coding classification
Chapter 40: Compressed-Domain Video Processing
Table 40.1: Mathematical expression of spatial vs. DCT domain algebraic operations
Table 40.2: Mathematical expression of distributiveness of DCT
Chapter 42: Video Watermarking Overview and Challenges
Table 42.1: Number of publications having the keyword watermarking as their main subject according to INSPEC database, July 2002.
Table 42.2: Video watermarking: applications and associated purpose
Table 42.3: Examples of nonhostile video processings
Table 42.4: Pros and cons of the different approaches for video watermarking.
Chapter 43: Creating Personalized Video Presentations using Multimodal Processing
Table 43.1: Parameters for different client devices.
Chapter 44: Segmenting Stories in News Video
Table 44.1: The classification results from the Decision Tree
Table 44.2: The results of HMM analysis in tests I & II
Table 44.3: Results of using combination of features used in Test II