Multi-label visual classification with label exclusive context

Xiangyu Chen, Xiao-Tong Yuan, Qiang Chen, Shuicheng YAN, Tat-Seng Chua

Abstract: We introduce in this paper a novel approach to multi-label image classification which incorporates a new type of context - label exclusive context - with linear representation and classification. Given a set of exclusive label groups that describe the negative relationship among class labels, our method, namely LELR for Label Exclusive Linear Representation, enforces repulsive assignment of the labels from each group to a query image. The problem can be formulated as an exclusive Lasso (eLasso) model with group overlaps and affine transformation. Since existing eLasso solvers are not directly applicable to solving such an variant of eLasso in our setting, we propose a Nesterov's smoothing approximation algorithm for efficient optimization. Extensive comparing experiments on the challenging real-world visual classification benchmarks demonstrate the effectiveness of incorporating label exclusive context into visual classification.