Multi-view semi-supervised learning for image classification

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• 作者：Songhao Zhu ^{zhush@njupt.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Xian SunAuthor Vitae ; Dongliang JinAuthor Vitae
• 关键词：Multi-view semi-supervised learning ; Maximum entropy ; Multi-view fusion ; Image classification
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 October 2016
• 年：2016
• 卷：208
• 期：Complete
• 页码：136-142
• 全文大小：4281 K

文摘

With the massive growth of digital image data uploaded to the Internet, classifying each image into appropriate semantic category with respect to its image content for image index and image retrieval has become an increasingly difficult and laborious task. To deal with this issue, we propose a novel multi-view semi-supervised learning framework which leverages the information contained in pseudo-labeled images to improve the prediction performance of image classification using multiple views of an image. In the training process, labeled images are first adopted to train view-specific classifiers independently using uncorrelated and sufficient views, and each view-specific classifier is then iteratively re-trained with respect to a measure of confidence using initial labeled samples and additional pseudo-labeled samples. In the classification process, the maximum entropy principle is utilized to assign appropriate category labels to unlabeled images via optimally trained view-specific classifiers. Experimental results on a general-purpose image database demonstrate the effectiveness and efficiency of the proposed multi-view semi-supervised image classification scheme.