34 citations · 34 across the 3 of their papers we have counts for
3 papers
cs.CV2016
Object Recognition Based on Amounts of Unlabeled Data
Fuqiang Liu, Fukun Bi, Liang Chen
This paper proposes a novel semi-supervised method on object recognition. First, based on Boost Picking, a universal algorithm, Boost Picking Teaching (BPT), is proposed to train a…
cs.CV2016★ 34 cited
Feature-Area Optimization: A Novel SAR Image Registration Method
Fuqiang Liu, Fukun Bi, Liang Chen +2
This letter proposes a synthetic aperture radar (SAR) image registration method named Feature-Area Optimization (FAO). First, the traditional area-based optimization model is recon…
cs.CV2016
Boost Picking: A Universal Method on Converting Supervised Classification to Semi-supervised Classification
Fuqiang Liu, Fukun Bi, Yiding Yang +1
This paper proposes a universal method, Boost Picking, to train supervised classification models mainly by un-labeled data. Boost Picking only adopts two weak classifiers to estima…