Multi-class object classifying method and system

摘要

A multi-class object classifying method and system are disclosed herein, where the multi-class object classifying method includes the following steps: classes, first training images and second training images are received and stored, and first characteristic images and second characteristic images are respectively extracted from the first training images and the second training images; the first training images is used to generate classifiers through a linear mapping classifying method; a classifier and the second characteristic images are used to determine parameter ranges corresponding to the classes and a threshold corresponding to the classifier. When two of the parameter ranges overlap, the remaining parameter ranges except for the two overlapped parameter ranges are recorded; after another classifier is selected from the classifiers except for the classifier that has been selected, the previous steps is repeated until the parameter ranges don't overlap with each other and the parameter ranges are recorded.

主权项

1. A multi-class object classifying method, comprising:
(a) receiving and storing a plurality of classes, a plurality of first training images and a plurality of second training images, extracting a plurality of first characteristic images from the first training images, and using the first training images to generate a plurality of classifiers corresponding to the classes through a linear mapping classifying method, wherein the first training images and the second training images respectively correspond to the classes; (b) extracting a plurality of second characteristic images from the second training images and selecting a classifier from the classifiers; (c) using the classifier and the second characteristic images to determine a plurality of parameter ranges corresponding to the classes and a threshold corresponding to the classifier; (d) when two of the parameter ranges overlap, recording the remaining parameter ranges except for the two parameter ranges, and after selecting another classifier from the classifiers except for the classifier that has been selected, repeating the step (c) until the parameter ranges don't overlap with each other; (e) when the parameter ranges don't overlap with each other, recording the parameter ranges; (f) receiving and storing a pending image and extracting a pending characteristic image from the pending image, and using a classifier and the pending image to determine a pending parameter; (g) when the pending parameter is larger than the threshold corresponding to the classifier, classifying the pending image into the class corresponding to the classifier; and (h) when the pending parameter lies in the parameter ranges, classifying the pending image into the classes corresponding to the parameter ranges.