您当前所在位置: 首页 > 学者

周志华

  • 42浏览

  • 0点赞

  • 0收藏

  • 0分享

  • 64下载

  • 0评论

  • 引用

期刊论文

Exploiting Unlabeled Data in Content-Based Image Retrieval

周志华Zhi-Hua Zhou Ke-Jia Chen and Yuan Jiang

ECML 2004, LNAI 3201, pp. 525-536, 2004.,-0001,():

URL:

摘要/描述

In this paper, the Ssair (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 the pool which is used in the next round of relevance feedback. Experiments show that semi-supervised learning and active learning mechanisms are both beneficial to Cbir.

关键词:

版权说明:以下全部内容由周志华上传于   2005年08月02日 17时43分53秒,版权归本人所有。

我要评论

全部评论 0

本学者其他成果

    同领域成果