Name
Abstract | ||
|---|---|---|
We present a noise resilient probabilistic model for active learning of a Gaussian process classifier from crowds, i.e., a set of noisy labelers. It explicitly models both the overall label noises and the expertise level of each individual labeler in two levels of flip models. Expectation propagation is adopted for efficient approximate Bayesian inference of our probabilistic model for classification, based on which, a generalized EM algorithm is derived to estimate both the global label noise and the expertise of each individual labeler. The probabilistic nature of our model immediately allows the adoption of the prediction entropy and estimated expertise for active selection of data sample to be labeled, and active selection of high quality labelers to label the data, respectively. We apply the proposed model for three visual recognition tasks, i.e., object category recognition, gender recognition, and multi-modal activity recognition, on three datasets with real crowd-sourced labels from Amazon Mechanical Turk. The experiments clearly demonstrated the efficacy of the proposed model. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICCV.2013.373 | ICCV |
Keywords | Field | DocType |
expertise estimation,active learning,expectation-maximisation algorithm,gaussian processes,gaussian process classifier,high-quality labeler active selection,bayesian inference,prediction entropy,expectation propagation,individual labeler,active visual recognition,inference mechanisms,generalized em algorithm,crowdsourcing,flip model,object category recognition,multimodal activity recognition,noise resilient probabilistic model,image recognition,data sample active selection,noisy labelers,gender recognition,active selection,crowd-sourced labels,classification,global label noise estimation,multi-modal activity recognition,visual recognition task,amazon mechanical turk,visual recognition,probabilistic model | Bayesian inference,Crowdsourcing,Computer science,Artificial intelligence,Expectation propagation,Probabilistic logic,Classifier (linguistics),Computer vision,Activity recognition,Pattern recognition,Expectation–maximization algorithm,Statistical model,Machine learning | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1550-5499 |
Citations | PageRank | References |
19 | 0.63 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengjiang Long | 1 | 107 | 14.21 |
| Gang Hua | 2 | 2796 | 157.90 |
| Ashish Kapoor | 3 | 1833 | 119.72 |