OBJECT CLASSIFICATION WITH CONSTRAINED MULTIPLE INSTANCE SUPPORT VECTOR MACHINE

摘要

This disclosure provides method and systems of classifying a digital image of an object. Specifically, according to one exemplary embodiment, an object classifier is trained using a constrained MI-SVM (multiple instance-support vector machine) approach whereby training images of objects are sampled to generate a collection of image regions associated with an object type and viewpoint, and the classifier is trained to determine an appropriate mid-level representation of the training image which is discriminative.

主权项

1. A computer implemented method of classifying a digital image of an object, the method comprising:
a) receiving a digital image of an object to be classified with a processor; and b) classifying the digital image with a constrained multiple-instance support vector machine (MI-SVM) classifier, the constrained MI-SVM classifier having been automatically trained using a plurality of training images, the training images including a plurality of object types from a plurality of viewpoints, each training image including an image of an object associated with one of the plurality of object types and one of the plurality of object viewpoints, an associated object type label and an associated viewpoint label, the constrained MI-SVM classifier trained by sampling each training image to generate a bag of image regions associated with each training image, discovering s discriminative image region associated with each training image, and generating a collection of discriminative image regions for each of the plurality of object types and each of the plurality of viewpoints.