Authors:
Dariusz Sychel
;
Przemysław Klęsk
and
Aneta Bera
Affiliation:
West Pomeranian University of Technology, Poland
Keyword(s):
Cascade of Clasifier, Detection, Expected Number of Features.
Related
Ontology
Subjects/Areas/Topics:
Classification
;
Feature Selection and Extraction
;
Model Selection
;
Pattern Recognition
;
Theory and Methods
Abstract:
A cascade of classifiers, working within a detection procedure, extracts and uses different number of features
depending on the window under analysis. Windows with background regions can be typically recognized as
negative with just a few features, whereas windows with target objects (or resembling them) might require
thousands of features. The central point of attention for this paper is a quantity that describes the average
computational cost of an operating cascade, namely—the expected value of the number of features the cascade
uses. This quantity can be calculated explicitly knowing the probability distribution underlying the data and
the properties of a particular cascade (detection and false alarm rates of its stages), or it can be accurately
estimated knowing just the latter. We show three purely geometric examples that demonstrate how training a
cascade with sensitivity / FAR constraints imposed per each stage can lead to non-optimality in terms of the
computational cost. We
do not propose a particular algorithm to overcome the pitfalls of stage-wise training,
instead, we sketch an intuition showing that non-greedy approaches can improve the resulting cascades.
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