By C, S Iliopoulos, K Park, K Steinhoefel
The current quantity is devoted to elements of algorithmic paintings in bioinformatics and computational biology with an emphasis on string algorithms that play a vital function within the research of organic sequences. The papers integrated are a variety of articles similar to talks given at one among conferences backed through The Royal Society, the UK's nationwide academy of technology, below supply no.: JEB/KOREAN Networks/16715. The provide supported workshops organised via researches from the Seoul nationwide college (Korea) and King's university London (UK). the 1st workshop was once held in Seoul, Korea, in July 2004 and the second one assembly came about in London, united kingdom, in February 2005 as a part of the yearly London Stringology Days.
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Additional resources for Algorithms in Bioinformatics
Vincent, N. (2000). Real time temporal segmentation of compressed and uncompressed dynamic colour image sequences. International Workshop on Real Time Image Sequence Analysis, 56-62. , & Vincent, N. (2003). A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval. Real-Time Imaging, 9(1), 73-98. Lienhart, R. (1999). Comparison of automatic shot boundary detection algorithms. SPIE, 290–301. 29 Video Representation and Processing for Multimedia Data Mining Ma, Y.
Survey of compressed-domain features used in audio-visual indexing and analysis. Journal of Visual Communication and Image Representation, 14(2), 150-183. 30 Video Representation and Processing for Multimedia Data Mining Yeo, B. , & Liu, B. (1995). Unified approach to temporal segmentation of motion JPEG and MPEG video. International Conference on Multimedia Computing and Systems, 2-13. , & Chia, L. T. (2006). A motion-based scene tree for browsing and retrieval of compressed videos. Information Systems, 31(7), 638-658.
However, the reality is that for some reasons, some shots can be missed, and also some non-existing boundaries may seem to be detected, which is called the false detection or false positive. One way of quantitatively evaluating the accuracy is by calculating the detection-rate and the error-rate (Tian & Zhang, 1999). The detection-rate is the percentage of the correctly detected shots, relative to the total number of shots. The total number of shots should be identified, somehow but usually manually, as a ground-truth.