Issue No. 07 - July (2011 vol. 33)
Ning Zhou , University of North Carolina, Charlotte
William K. Cheung , Hong Kong Baptist University, Hong Kong
Guoping Qiu , University of Nottingham, Nottingham
Xiangyang Xue , Fudan University, Shanghai
The increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level image features and high-level user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based solely on the low-level image features. For images with user provided tags, HPM jointly exploits both the image features and the tags in a unified probabilistic framework to recommend additional tags to label the images. The HPM framework makes use of the tag-image association matrix (TIAM). However, since the number of images is usually very large and user-provided tags are diverse, TIAM is very sparse, thus making it difficult to reliably estimate tag-to-tag co-occurrence probabilities. We developed a collaborative filtering method based on nonnegative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L_1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The effectiveness of the proposed approach has been evaluated using three databases containing 5,000 images with 371 tags, 31,695 images with 5,587 tags, and 269,648 images with 5,018 tags, respectively.
Automatic image tagging, collaborative filtering, feature integration, nonnegative matrix factorization, kernel density estimation.
X. Xue, N. Zhou, G. Qiu and W. K. Cheung, "A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 1281-1294, 2010.