A hybrid probabilistic model for unified collaborative and content-based image tagging

Ning Zhou*, Kwok Wai CHEUNG, Guoping Qiu, Xiangyang Xue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

84 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number5639019
Pages (from-to)1281-1294
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume33
Issue number7
DOIs
Publication statusPublished - 2011

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

User-Defined Keywords

  • Automatic image tagging
  • collaborative filtering
  • feature integration
  • kernel density estimation
  • nonnegative matrix factorization

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