西北师范大学学报（自然科学版）2016，Vol.52Issue(6)：56-63,8.DOI:10.16783/j.cnki.nwnuz.2016.06.012
基于联合子空间与多源适应学习的多标签视觉分类
Multi-label visual classification based on joint subspace and multi-source adaptation learning
摘要
Abstract
Traditional visual classification methods usually ignore the correlations among different tags, and the discriminative information existed in lots of related auxiliary source domains.In this paper on the basis of the advances of shared subspace and multi-source adaptation learning research, a novel joint shared subspace learning and multi-source adaptation multi-label (MSML)visual classification method is proposed.Specifically,MSML simultaneously considers the label correlation,flexible feature similarity embedding,and multi-source model adaptation,and integrates them into a unified learning model.The results show that the globally optimal solution of the proposed method can be obtained by performing generalized eigen-decomposition.We evaluate the proposed method on two real-world visual classification tasks such as video concept detection and automatic image annotation.The experimental results show that the proposed algorithm is effective and can obtain comparable or even superior to related works.关键词
共享子空间学习/多源适应学习/视觉分类/多标签学习Key words
shared subspace learning/multi-source adaptation learning/visual classification/multi-label learning分类
信息技术与安全科学引用本文复制引用
严良达,陶剑文..基于联合子空间与多源适应学习的多标签视觉分类[J].西北师范大学学报（自然科学版）,2016,52(6):56-63,8.基金项目
教育部人文社科基金资助项目(13YJAZH084) （13YJAZH084）
浙江省自然科学基金资助项目(LY14F020009) （LY14F020009）
宁波市自然科学基金资助项目(2014A610024) （2014A610024）
