Patent ID: 8548260

Claim:
A learning apparatus configured to learn a voting position in a parameter space and a first classifier in an object detecting apparatus, which discriminates whether feature points detected in an input image belong to a detection target object or not by the first classifier using a plurality of random trees and detects the detection target object by voting for its parameters using the feature points discriminated as belonging to the detection target object, comprising: a feature point detecting unit configured to detect a feature point of the detection target object and a feature point of a non detection target object in the training image respectively; a feature value calculating unit configured to calculate feature values in a peripheral image area of the respective feature points detected from the training image, label the feature value calculated from the peripheral image area of the feature point extracted in the detection target object with a label indicating a class of the detection target object, and label the feature value calculated from the peripheral image area of the feature point extracted in the non detection target object with a label indicating the non detection target object; a vote learning unit configured to calculate the voting position in the parameter space by the relative position of the feature point of the detection target object from the detection target object on the training image; and a classifier learning unit configured to learn the first classifier using the labeled feature values of the training image so that a class distribution of the class is concentrated and the voting positions in the parameter space are concentrated, at nodes at deeper positions in the random tree, wherein the classifier learning unit: splits a parent node of the random tree using a weighted sum of a degree of reduction of an entropy of the class distribution and a degree of reduction of a variance of the voting positions in the parameter space; and the weight to the degree of reduction of the variance of the voting positions is a function of a detection target object ratio, and the detection target object ratio is a ratio of the number of feature values corresponding to the detection target object to the number of all of the feature values which belong to the nodes of the random trees.