Multi-view active learning is a technique which can realize more significant reduction on version space than traditional active learning and has been used in large-scale data analysis.This paper proposes two improvements in both hypothesis generation and sampling strategy.We integrate boosting-like idea into the active learning framework which uses the weighted voting of all hypothetic outputs from the past queries.Furthermore

a novel adaptive hierarchical competition sampling is presented.In this sampling strategy

if the number of the contention samples is large

an unsupervised spectral clustering is activated to obtain the coarse distribution of these contention samples in the feature space and then both the classification uncertainty and redundancy measures are considered in each cluster to query the unlabeled samples in batch mode by solving quadratic programming.We apply multi-view active learning in image classification in order to prove the effectiveness of the improvements and different image features are used as views to generate the corresponding hypothesis.The experiments demonstrate that our two proposals can both efficiently improve the performance of the multi-view active learning and the random combination of these views can also obtain faster convergence and better classification accuracy than state-of-the-art single-view active learning algorithms.