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Boosting attribute recognition with latent topics by matrix factorization
Author(s) -
Su Zhuo,
Li Donghui,
Li Hanhui,
Luo Xiaonan
Publication year - 2017
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23827
Subject(s) - computer science , boosting (machine learning) , artificial intelligence , non negative matrix factorization , pattern recognition (psychology) , machine learning , matrix decomposition , task (project management) , cognitive neuroscience of visual object recognition , feature extraction , eigenvalues and eigenvectors , physics , quantum mechanics , management , economics
Attribute‐based approaches have recently attracted much attention in visual recognition tasks. These approaches describe images by using semantic attributes as the mid‐level feature. However, low recognition accuracy becomes the biggest barrier that limits their practical applications. In this paper, we propose a novel framework termed Boosting Attribute Recognition (BAR) for the image recognition task. Our framework stems from matrix factorization, and can explore latent relationships from the aspect of attribute and image simultaneously. Furthermore, to apply our framework in large‐scale visual recognition tasks, we present both offline and online learning implementation of the proposed framework. Extensive experiments on 3 data sets demonstrate that our framework achieves a sound accuracy of attribute recognition.