Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

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

Language

English

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 L1 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.

Keywords

kernel density estimation., Automatic image tagging, collaborative filtering, feature integration, nonnegative matrix factorization, Correlation, Collaboration, Tagging, Probabilistic logic, Visualization, Semantics, Hidden Markov models

Publication Date

7-2011

Source Publication Title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

33

Issue

7

Start Page

1281

End Page

1294

Publisher

Institute of Electrical and Electronics Engineers

Peer Reviewed

1

Funder

This work was supported in part by the HKBU science Faculty Research Student Exchange Program, the 973 Program (No. 2010CB327900, the NSF of China (No. 60873178), and the Shanghai Leading Academic Discipline Project (No. B114).

DOI

10.1109/TPAMI.2010.204

Link to Publisher's Edition

http://dx.doi.org/10.1109/TPAMI.2010.204

ISSN (print)

01628828

ISSN (electronic)

19393539

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