David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
In 18th IEEE International Conference on Image Processing. IEEE (2011)
We present a minimum message length (MML) framework for trajectory partitioning by point selection, and use it to automatically select the tolerance parameter ε for Douglas-Peucker partitioning, adapting to local trajectory complexity. By examining a range of ε for synthetic and real trajectories, it is easy to see that the best ε does vary by trajectory, and that the MML encoding makes sensible choices and is robust against Gaussian noise. We use it to explore the identification of micro-activities within a longer trajectory. This MML metric is comparable to the TRACLUS metric – and shares the constraint of abstracting only by omission of points – but is a true lossless encoding. Such encoding has several theoretical advantages – particularly with very small segments (high frame rates) – but actual performance interacts strongly with the search algorithm. Both differ from unconstrained piecewise linear approximations, including other MML formulations.
|Keywords||MML Minimum Message Length Trajectory Partitioning Compression Segmentation Encoding MDL Minimum Description Length Abstraction|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
David L. Dowe, Steve Gardner & and Graham Oppy (2007). Bayes Not Bust! Why Simplicity Is No Problem for Bayesians. British Journal for the Philosophy of Science 58 (4):709 - 754.
Charles Twardy, Steve Gardner & David Dowe (2005). Empirical Data Sets Are Algorithmically Compressible: Reply to McAllister. Studies in the History and Philosophy of Science, Part A 36 (2):391-402.
Michael Alekhnovich, Sam Buss, Shlomo Moran & Toniann Pitassi (2001). Minimum Propositional Proof Length is NP-Hard to Linearly Approximate. Journal of Symbolic Logic 66 (1):171-191.
David Dowe & Graham Oppy (2001). Universal Bayesian Inference? Behavioral and Brain Sciences 24 (4):662-663.
José Hernández-Orallo & Ismael García-Varea (2000). Explanatory and Creative Alternatives to the MDL Priciple. Foundations of Science 5 (2):185-207.
T. M. Wilkinson (2004). The Ethics and Economics of the Minimum Wage. Economics and Philosophy 20 (2):351-374.
Gary Hatfield & William Epstein (1985). The Status of the Minimum Principle in the Theoretical Analysis of Visual Perception. Psychological Bulletin 97 (2):155–186.
Frank Henmueller & Karl Menger (1961). What is Length? Philosophy of Science 28 (2):172-177.
Bruce Edmonds (1995). What is Complexity? - The Philosophy of Complexity Per Se with Application to Some Examples in Evolution. In [Book Chapter] (in Press).
Patrick Cordier, Michel Mendès France, Philippe Bolon & Jean Pailhous (1994). Thermodynamic Study of Motor Behaviour Optimization. Acta Biotheoretica 42 (2-3):187-201.
Alexander Razborov (2002). Review: Michael Alekhnovich, Sam Buss, Shlomo Moran, Toniann Pitassi, Minimum Propositional Proof Length Is NP-Hard to Linearly Approximate. [REVIEW] Bulletin of Symbolic Logic 8 (2):301-302.
Vivien Robinet, Benoît Lemaire & Mirta B. Gordon (2011). MDLChunker: A MDL-Based Cognitive Model of Inductive Learning. Cognitive Science 35 (7):1352-1389.
Added to index2012-04-01
Total downloads182 ( #19,834 of 1,911,319 )
Recent downloads (6 months)60 ( #8,836 of 1,911,319 )
How can I increase my downloads?