Abstract Semantic attributes have been proposed to bridge the semantic gap between low-level feature representation and high-level semantic understanding of visual objects. Obtaining a good representation of semantic attributes usually requires learning from high-dimensional low-level features, which not only significantly increases the time and space requirement but also degrades the performance due to numerous irrelevant features. Since multiattribute prediction can be generalized as an multitask learning problem, sparse-based multi-task feature selection approaches have been introduced, utilizing the relatedness among multiple attributes. However, such approaches either do not investigate the pattern of the relatedness among attributes, or require prior knowledge about the pattern. In this paper, we propose a novel feature selection approach which embeds attribute correlation modeling in multi-attribute joint feature selection. Experiments on both synthetic dataset and multiple public benchmark datasets demonstrate that the proposed approach effectively captures the correlation among multiple attributes and significantly outperforms the state-of-the-art approaches.