Exploiting Unlabeled Data in Content-Based Image Retrieval
Exploiting Unlabeled Data in Content-Based Image Retrieval
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DOI：
10.1007/978-3-540-30115-8_48
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发表时间：
2004-09
期刊：
影响因子：
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通讯作者：
Zhi-Hua Zhou;Ke Chen;Yuan Jiang
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文献类型：
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作者：
Zhi-Hua Zhou;Ke Chen;Yuan Jiang
本文提出了半监督主动图像检索（Semi-Supervised Active Image Retrieval，Ssair）方法，该方法试图利用未标记数据来提高基于内容的图像检索（Content-Based Image Retrieval，Cbir）的性能。这种方法结合了半监督学习和主动学习的优点。详细地说，在每一轮的相关反馈，两个简单的学习者训练的标记数据，即图像从用户查询和用户反馈。然后，每个学习者对数据库中的未标记图像进行分类，并将最相关/最不相关的图像传递给其他学习者。在使用额外的标记数据重新训练之后，学习器再次对数据库中的图像进行分类，然后合并它们的分类。将置信度高的相关图像作为检索结果返回，而置信度低的相关图像则放入池中，用于下一轮的相关反馈。实验结果表明，半监督学习和主动学习机制都有利于Cbir.
In this paper, theSsair(Semi-Supervised Active Image Retrieval) approach, which attempts to exploit unlabeled data to improve the performance of content-based image retrieval (Cbir), is proposed. This approach combines the merits of semi-supervised learning and active learning. In detail, in each round of relevance feedback, two simple learners are trained from the labeled data, i.e. images from user query and user feedback. Each learner then classifies the unlabeled images in the database and passes the most relevant/irrelevant images to the other learner. After re-training with the additional labeled data, the learners classify the images in the database again and then their classifications are merged. Images judged to be relevant with high confidence are returned as the retrieval result, while these judged with low confidence are put into thepoolwhich is used in the next round of relevance feedback. Experiments show that semi-supervised learning and active learning mechanisms are both beneficial toCbir.