Mining Social Images with Distance Metric Learning for Automated Image Tagging

• Main Authors: WU, Pengcheng, HOI, Steven C. H., ZHAO, Peilin, HE, Ying
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: distance metric learning; inductive learning; social images; automated image tagging; transductive learning; Computer Sciences; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/2352; https://ink.library.smu.edu.sg/context/sis_research/article/3352/viewcontent/Mining_Social_Images_with_Distance_Metric_Learning_for_Automated_Image_Tagging.pdf
• Institution: Singapore Management University

With the popularity of various social media applications, massive social images associated with high quality tags have been made available in many social media web sites nowadays. Mining social images on the web has become an emerging important research topic in web search and data mining. In this paper, we propose a machine learning framework for mining social images and investigate its application to automated image tagging. To effectively discover knowledge from social images that are often associated with multimodal contents (including visual images and textual tags), we propose a novel Unified Distance Metric Learning (UDML) scheme, which not only exploits both visual and textual contents of social images, but also effectively unifies both inductive and transductive metric learning techniques in a systematic learning framework. We further develop an efficient stochastic gradient descent algorithm for solving the UDML optimization task and prove the convergence of the algorithm. By applying the proposed technique to the automated image tagging task in our experiments, we demonstrate that our technique is empirically effective and promising for mining social images towards some real applications.