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Alternative TitleLow-rank constraint online self-supervised learning scene classification method
丛杨; 宋红玉; 唐延东
Rights Holder中国科学院沈阳自动化研究所
Patent Agent沈阳科苑专利商标代理有限公司 21002
Other AbstractThe invention relates to a low-rank constraint online self-supervised learning scene classification method. The method comprises the following steps: performing training and feature extraction on off-line image data; carrying out small-batch training to obtain an initial metric learner; inputting online data images sequentially and extracting image features; judging whether each image feature has a label; if the image feature has the label, updating the metric learner; if the image feature has no label, measuring the similarity between the image feature and each training sample, and utilizing a generated bidirectional linear graph to transmit the label; judging feature vector similarity scores of the sample; if the scores are high, updating the metric learner; and otherwise, inputting online data images. According to the scene classification method, self-updating can be realized gradually and useful information obtained from marked samples and unmarked samples can be combined; and the framework of a unified on-line self-updating model is utilized to process online scene classification, so that the on-line automatic scene classification can be achieved, the accuracy of classification is ensured, and work efficiency is improved.
PCT Attributes
Application Date2012-10-31
Date Available2017-09-15
Application NumberCN201210429630.1
Open (Notice) NumberCN103793713A
Contribution Rank1
Document Type专利
Recommended Citation
GB/T 7714
丛杨,宋红玉,唐延东. 低秩约束的在线自监督学习的场景分类方法[P]. 2014-05-14.
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