A unified learning framework for auto face annotation by mining web facial images

• Main Authors: WANG, Dayong, HOI, Steven C. H., HE, Ying
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: face annotation; image retrieval; inductive learning; sparse coding; transductive learning; web facial images; Computer Sciences; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/2345; https://ink.library.smu.edu.sg/context/sis_research/article/3345/viewcontent/p1392_wang.pdf
• Institution: Singapore Management University

Auto face annotation plays an important role in many real-world multimedia information and knowledge management systems. Recently there is a surge of research interests in mining weakly-labeled facial images on the internet to tackle this long-standing research challenge in computer vision and image understanding. In this paper, we present a novel unified learning framework for face annotation by mining weakly labeled web facial images through interdisciplinary efforts of combining sparse feature representation, content-based image retrieval, transductive learning and inductive learning techniques. In particular, we first introduce a new search-based face annotation paradigm using transductive learning, and then propose an effective inductive learning scheme for training classification-based annotators from weakly labeled facial images, and finally unify both transductive and inductive learning approaches to maximize the learning efficacy. We conduct extensive experiments on a real-world web facial image database, in which encouraging results show that the proposed unified learning scheme outperforms the state-of-the-art approaches