Active learning for tag recommendation utilizing on-line photos lacking tags

Yajun Gao, Baoxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recommending text tags for on-line photos is useful for Internet photo services. Typical solutions to this problem require analysis of the correlation among different attributes of the photos, including the correlation between the textual features and visual features computed from a photo. However, most on-line photos have very few tags or even no tags, and thus they contribute little or none to the analysis of tag-photo correlation, which is a key component in those schemes that rely on such analysis for tag recommendation. To address this practical challenge, we propose an active learning method for incorporating photos with no or few tags so as to enhance the correlation analysis for improved performance in tag recommendation. We demonstrate the effectiveness of the proposed approach using a dataset of more than 33,000 photos collected from Flickr.

Original languageEnglish (US)
Title of host publication2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Pages2869-2872
Number of pages4
DOIs
StatePublished - 2012
Event2012 19th IEEE International Conference on Image Processing, ICIP 2012 - Lake Buena Vista, FL, United States
Duration: Sep 30 2012Oct 3 2012

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2012 19th IEEE International Conference on Image Processing, ICIP 2012
CountryUnited States
CityLake Buena Vista, FL
Period9/30/1210/3/12

Keywords

  • Tag recommendation
  • active learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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