Noisy Label Recovery For Shadow Detection In Unfamiliar Domains

2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)(2016)

引用 45|浏览0
暂无评分
摘要
Recent shadow detection algorithms have shown initial success on small datasets of images from specific domains. However, shadow detection on broader image domains is still challenging due to the lack of annotated training data. This is due to the intense manual labor in annotating shadow data. In this paper we propose "lazy annotation", an efficient annotation method where an annotator only needs to mark the important shadow areas and some non-shadow areas. This yields data with noisy labels that are not yet useful for training a shadow detector. We address the problem of label noise by jointly learning a shadow region classifier and recovering the labels in the training set. We consider the training labels as unknowns and formulate the label recovery problem as the minimization of the sum of squared leave-one-out errors of a Least Squares SVM, which can be efficiently optimized. Experimental results show that a classifier trained with recovered labels achieves comparable performance to a classifier trained on the properly annotated data. These results suggest a feasible approach to address the task of detecting shadows in an unfamiliar domain: collecting and lazily annotating some images from the new domain for training. As will be demonstrated, this approach outperforms methods that rely on precisely annotated but less relevant datasets. Initial results suggest more general applicability.
更多
查看译文
关键词
noisy label recovery,shadow detection algorithm,image dataset,lazy annotation method,shadow data annotataion,label recovery problem,least squares SVM,support vector machines
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要