Collaborative and content-based image labeling

Many on-line photo sharing systems allow users to tag their images so as to support semantic image search. In this paper, we study how one can take advantages of the already-tagged images to (semi-)automate the labeling of newly uploaded ones. In particular, we propose a hybrid approach for the prediction where user-provided tags and image visual contents are fused under a unified probabilistic framework. Kernel smoothing and collaborative filtering techniques are explored for improving the accuracy of the probabilistic models estimation. By comparing with some stateof-the-art content-based image labeling methods, we have empirically shown that 1) the proposed method can achieve comparable tag prediction accuracy when there is no user-provided tag, and that 2) it can significantly boost the prediction accuracy if the user can provide just a few tags.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Ning Zhou, W. K. Cheung, Xiangyang Xue, Guoping Qiu
• Keywords: Computer Vision, ICPR 2008, Prediction Accuracy, Tag Prediction Accuracy, User-provided Tag