Attribute-based people search in surveillance environments
Daniel A. Vaquero, Rogerio S. Feris, et al.
WACV 2009
In this paper we introduce a learning approach to improve the efficiency of manual image annotation. Although important in practice, manual image annotation has rarely been studied in a quantitative way. We propose formal models to characterize the annotation times for two commonly used manual annotation approaches, i.e., tagging and browsing. The formal models make clear the complementary properties of these two approaches, and inspire a learning-based hybrid annotation algorithm. Our experiments show that the proposed algorithm can achieve up to a 50% reduction in annotation time over baseline methods. ©2008 IEEE.
Daniel A. Vaquero, Rogerio S. Feris, et al.
WACV 2009
Conrad Albrecht, Jannik Schneider, et al.
CVPR 2025
Pavel Kisilev, Daniel Freedman, et al.
ICPR 2012
Sudeep Sarkar, Kim L. Boyer
Computer Vision and Image Understanding