Domain adaptation for image classification with class priors
US9710729B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 4, 2014 |
| Grant date | Jul 18, 2017 |
| Priority date | — |
| Expiry date | Feb 25, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/54
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In camera-based object labeling, boost classifier ƒT(x)=Σr=1Mβrhr(x) is trained to classify an image represented by feature vector x using a target domain training set DT of labeled feature vectors representing images acquired by the same camera and a plurality of source domain training sets DS, . . . , DSacquired by other cameras. The training applies an adaptive boosting (AdaBoost) algorithm to generate base classifiers hr(x) and weights βr. The rth iteration of the AdaBoost algorithm trains candidate base classifiers hrk(x) each trained on a training set DT∪DS, and selects hr(x) from previously trained candidate base classifiers. The target domain training set DT may be expanded based on a prior estimate of the labels distribution for the target domain. The object labeling system may be a vehicle identification system, a machine vision article inspection system, or so forth.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.