Heterogeneous domain adaptation method for video annotation. Issue 2 (29th November 2016)

• Record Type: Journal Article
• Authors: Wang, Han; Wu, Xinxiao; Jia, Yunde
• Is Part Of: IET computer vision. Volume 11:Issue 2(2017)
• Page Start: 181
• Page End: 187
• Publication Date: 2016-11-29
• Subjects: video signal processing -- learning (artificial intelligence) -- correlation methods -- matrix algebra -- feature extraction; heterogeneous domain adaptation method -- video annotation problem -- image source domain -- video target domain -- feature learning method -- heterogeneous discriminative analysis-of-canonical correlation -- HDCC -- discriminative information -- topology information -- projection matrices -- group weighting learning framework -- multidomain adaptation -- CCV dataset -- Kodak dataset; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2016.0148
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : In this study, the authors study the video annotation problem over heterogeneous domains, in which data from the image source domain and the video target domain is represented by heterogeneous features with different dimensions and physical meanings. A novel feature learning method, called heterogeneous discriminative analysis of canonical correlation (HDCC), is proposed to discover a common feature subspace in which heterogeneous features can be compared. The HDCC utilises discriminative information from the source domain as well as topology information from the target domain to learn two different projection matrices. By using these two matrices, heterogeneous data can be projected onto a common subspace and different features can be compared. They additionally design a group weighting learning framework for multi‐domain adaptation to effectively leverage knowledge learned from the source domain. Under this framework, source domain images are organised in groups according to their semantic meanings, and different weights are assigned to these groups according to their relevancies to the target domain videos. Extensive experiments on the Columbia Consumer Video and Kodak datasets demonstrate the effectiveness of their HDCC and group weighting methods.