1. A method of objects detection for detecting different objects in an input image, the method comprising the steps of: preparing a plurality of weak classifiers each of which selects a vector in an N−1 (N≧3) dimensional histogram, the vector corresponding to values of characteristic quantities related to distribution of luminance calculated from each of partial images of a predetermined size cut from the input image, the N−1 dimensional histogram generated by: obtaining N histograms of values of the characteristic quantities for predetermined objects of N types through calculation of the characteristic quantities for each of the N types from different sample images representing the predetermined objects; converting values of frequency in the respective N histograms into vectors by linearly combining the values of frequency with predetermined N basis vectors corresponding to the N histograms one to one, the basis vectors having the same magnitude and isotropy in an N−1 dimensional space; and obtaining the N−1 dimensional histogram by combining the vectors of frequency for the values of the characteristic quantities corresponding to each other between the N histograms; cutting the partial images of the predetermined size at different positions in the input image; and carrying out judgment as to which one of the N types of the predetermined objects each of the partial images represents by: combining the vector or vectors selected by at least one of the weak classifiers by applying the classifier or classifiers on each of the partial images; extracting components of the basis vectors of the combined vector as scores respectively representing probabilities of the corresponding partial image being the predetermined objects of the N types corresponding to the basis vectors; and carrying out the judgment based on magnitude of the scores.