Boosting attribute recognition with latent topics by matrix factorization
提出BAR框架，通过矩阵分解同时探索属性和图像的潜在关系，提升图像识别中属性识别的准确率，并支持大规模任务的离线与在线学习。
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.