Abstract

Multi-label image classification aims to predict multiplelabels for a single image which contains diverse content. Byutilizing label correlations, various techniques have been developed to improve classification performance. However,current existing methods either neglect image features whenexploiting label correlations or lack the ability to learnimage-dependent conditional label structures. In this paper,we develop conditional graphical Lasso (CGL) to handle these challenges. CGL provides a unified Bayesian framework for structure and parameter learning conditioned on image features. We formulate the multi-label prediction asCGL inference problem, which is solved by a mean field variational approach. Meanwhile, CGL learning is efficient due to a tailored proximal gradient procedure by applying the maximum a posterior (MAP) methodology. CGL performs competitively for multi-label image classification on benchmark datasets MULAN scene, PASCAL VOC 2007 and PASCAL VOC 2012, compared with the state-of-the-art multi-label classification algorithms.