Mathematical properties of a sensitivity measure for quantifying feature variation

Stephen DelMarco

doi:10.1117/12.918508

8 May 2012 Mathematical properties of a sensitivity measure for quantifying feature variation

Stephen DelMarco

Proceedings Volume 8406, Mobile Multimedia/Image Processing, Security, and Applications 2012; 84060F (2012) https://doi.org/10.1117/12.918508
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Feature extraction is a key component of typical pattern recognition algorithms. Usually performance of feature extractors is governed by several parameters. Characterizing parameter value effect on feature extraction performance is valuable for aiding in appropriate parameter value selection. Often, the parameter space is discretized and the effect of discrete parameter values on feature variation is analyzed. However, it can be problematic to determine a discretization density that contains suitable parameter values. To address this issue, this paper further explores a previously-introduced sensitivity measure for quantifying feature variation as a function of parameter space sampling density. Further mathematical properties of the sensitivity measure are determined. Closed form expressions for special feature set relationships are derived. We investigate sensitivity measure convergence properties as a function of increasing parameter space sampling density. We present conditions for sensitivity measure convergence, and provide closed form expressions for the limiting values. We show how sensitivity measure convergence can be used to choose an appropriate parameter space sampling density. Numerical examples of sensitivity measure convergence, validating the theoretical results, are presented for feature extraction on natural imagery.

Citation Download Citation

Stephen DelMarco "Mathematical properties of a sensitivity measure for quantifying feature variation", Proc. SPIE 8406, Mobile Multimedia/Image Processing, Security, and Applications 2012, 84060F (8 May 2012); https://doi.org/10.1117/12.918508

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KEYWORDS

Lithium

Feature extraction

3D modeling

Data modeling

Detection and tracking algorithms

Pattern recognition

Edge detection

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