By Sudan M.
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Extra resources for Algorithmic Introduction to Coding Theory
Addison-Wesley, Harlow, England, 1999.  K. Brown, A rich diet: Data-rich multimedia has a lot in store for archiving and storage companies, Broadband Week, March 5, 2001.  B. Prabhakaran, Multimedia Database Management Systems, Kluwer, Norwell, MA, 1997.  S. Santini, and R. Jain, Image databases are not databases with images”, Proc. 9th International Conference on Image Analysis and Processing (ICIAP '97), Florence, Italy, September 17-19, 1997. S. Subrahmanian, Principles of Multimedia Database Systems, Morgan Kaufmann, San Francisco, CA, 1998.
2] allow user-defined templates of semantics in image sequences. Brand et al.  use coupled HMMs to model complex actions in Tai Chi movies. Recent work in segmentation and classification of audio streams includes [7,8,9,10,11,12,31,32]. Naphade and Huang  used hidden Markov models (HMMs) for representing the probability density functions of auditory features computed over a time series. Zhang and Kuo  used features based on heuristics for audio classification. HMMs have been successfully applied in speech recognition.
Therefore, unlike textual data, generating content-based video data indexes automatically is much harder. The process of building indexes for video programs can be divided into three main steps: 1. Parsing: temporal segmentation of the video contents into smaller units. 2. Abstraction: extracting or building a representative subset of video data from the original video. 3. Content analysis: extracting visual features from representative video frames. Existing work on video indexing can be classified in three categories : 1.
Algorithmic Introduction to Coding Theory by Sudan M.