David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
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.
William Epstein & Gary Hatfield, The Status of the Minimum Principle in the Theoretical Analysis of Visual Perception.
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).
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 downloads60 ( #26,034 of 1,102,926 )
Recent downloads (6 months)12 ( #16,268 of 1,102,926 )
How can I increase my downloads?