55 citations · 93 across the 7 of their papers we have counts for
7 papers
A Novel VHR Image Change Detection Algorithm Based on Image Fusion and Fuzzy C-Means Clustering
Rongcui Dong, Haoxiang Wang
This thesis describes a study to perform change detection on Very High Resolution satellite images using image fusion based on 2D Discrete Wavelet Transform and Fuzzy C-Means clust…
Super-resolution Reconstruction of SAR Image based on Non-Local Means Denoising Combined with BP Neural Network
Zeling Wu, Haoxiang Wang
In this article, we propose a super-resolution method to resolve the problem of image low spatial because of the limitation of imaging devices. We make use of the strong non-linear…
Semi-supervised structured output prediction by local linear regression and sub-gradient descent
Ru-Ze Liang, Wei Xie, Weizhi Li +3
We propose a novel semi-supervised structured output prediction method based on local linear regression in this paper. The existing semi-supervise structured output prediction meth…
Optimizing Top Precision Performance Measure of Content-Based Image Retrieval by Learning Similarity Function
Ru-Ze Liang, Lihui Shi, Haoxiang Wang +4
In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important comp…
Multiple kernel multivariate performance learning using cutting plane algorithm
Jingbin Wang, Haoxiang Wang, Yihua Zhou +1
In this paper, we propose a multi-kernel classifier learning algorithm to optimize a given nonlinear and nonsmoonth multivariate classifier performance measure. Moreover, to solve…
Image tag completion by local learning
Jingyan Wang, Yihua Zhou, Haoxiang Wang +3
The problem of tag completion is to learn the missing tags of an image. In this paper, we propose to learn a tag scoring vector for each image by local linear learning. A local lin…
Supervised cross-modal factor analysis for multiple modal data classification
Jingbin Wang, Yihua Zhou, Kanghong Duan +2
In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of d…