Papageorgiou, C.P.
Oren, M.
Poggio, T.
Artificial Intelligence Lab., MIT, Cambridge, MA ;
This paper appears in: Computer Vision, 1998. Sixth International Conference on
Publication Date: 4-7 Jan 1998
On page(s): 555-562
Meeting Date: 01/04/1998 - 01/07/1998
Location: Bombay, India
ISBN: 81-7319-221-9
References Cited: 21
INSPEC Accession Number: 6015985
DOI: 10.1109/ICCV.1998.710772
Posted online: 2002-08-06 21:56:49.0
Abstract
This paper presents a general trainable framework for object
detection in static images of cluttered scenes. The detection technique
we develop is based on a wavelet representation of an object class
derived from a statistical analysis of the class instances. By learning
an object class in terms of a subset of an overcomplete dictionary of
wavelet basis functions, we derive a compact representation of an object
class which is used as an input to a support vector machine classifier.
This representation overcomes both the problem of in-class variability
and provides a low false detection rate in unconstrained environments.
We demonstrate the capabilities of the technique in two domains whose
inherent information content differs significantly. The first system is
face detection and the second is the domain of people which, in contrast
to faces, vary greatly in color, texture, and patterns. Unlike previous
approaches, this system learns from examples and does not rely on any a
priori (hand-crafted) models or motion-based segmentation. The paper
also presents a motion-based extension to enhance the performance of the
detection algorithm over video sequences. The results presented here
suggest that this architecture may well be quite general
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