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Object Detection Using the Statistics of Parts (2004)
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BibTeX
@MISC{Schneiderman04objectdetection,
author = {Henry Schneiderman and Takeo Kanade},
title = { Object Detection Using the Statistics of Parts},
year = {2004}
}
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Abstract
In this paper we describe a trainable object detector and its instantiations for detecting faces and cars at any size, location, and pose. To cope with variation in object orientation, the detector uses multiple classifiers, each spanning a different range of orientation. Each of these classifiers determines whether the object is present at a specified size within a fixed-size image window. To find the object at any location and size, these classifiers scan the image exhaustively. Each classifier is based on the statistics of localized parts. Each part is a transform from a subset of wavelet coefficients to a discrete set of values. Such parts are designed to capture various combinations of locality in space, frequency, and orientation. In building each classifier, we gathered the class-conditional statistics of these part values from representative samples of object and non-object images. We trained each classifier to minimize classification error on the training set by using Adaboost with Confidence-Weighted Predictions (Shapire and Singer, 1999). In detection, each classifier computes the part values within the image window and looks up their associated classconditional probabilities. The classifier then makes a decision by applying a likelihood ratio test. For efficiency, the classifier evaluates this likelihood ratio in stages. At each stage, the classifier compares the partial likelihood ratio to a threshold and makes a decision about whether to cease evaluation—labeling the input as non-object—or to continue further evaluation. The detector orders these stages of evaluation from a low-resolution to a high-resolution search of the image. Our trainable object detector achieves reliable and efficient detection of human faces and passenger cars with out-of-plane rotation.
Keyphrases
object detection part value trainable object detector achieves confidence-weighted prediction trainable object detector high-resolution search object orientation associated classconditional probability class-conditional statistic localized part fixed-size image window likelihood ratio test representative sample various combination classification error passenger car discrete set out-of-plane rotation wavelet coefficient multiple classifier image window efficient detection detector order different range specified size non-object image partial likelihood ratio likelihood ratio
