Image annotation by large-scale content-based image retrieval

ABSTRACT

Image annotation has been an active research topic in recent years due to its potentially large impact on both image understanding and Web image search. In this paper, we target at solving the automatic image annotation problem in a novel search and mining framework. Given an uncaptioned image, first in the search stage, we perform content-based image retrieval (CBIR) facilitated by high-dimensional indexing to find a set of visually similar images from a large-scale image database. The database consists of images crawled from the World Wide Web with rich annotations, e.g. titles and surrounding text. Then in the mining stage, a search result clustering technique is utilized to find most representative keywords from the annotations of the retrieved image subset. These keywords, after salience ranking, are finally used to annotate the uncaptioned image. Based on search technologies, this framework does not impose an explicit training stage, but efficiently leverages large-scale and well-annotated images, and is potentially capable of dealing with unlimited vocabulary. Based on 2.4 million real Web images, comprehensive evaluation of image annotation on Corel and U. Washington image databases show the effectiveness and efficiency of the proposed approach.

REFERENCES

• 1: Kobus Barnard , Pinar Duygulu , David Forsyth , Nando de Freitas , David M. Blei , Michael I. Jordan, Matching words and pictures, The Journal of Machine Learning Research, 3, 3/1/2003
• 2: Gustavo Carneiro , Nuno Vasconcelos, A database centric view of semantic image annotation and retrieval, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076129]
• 3: P. Duygulu , Kobus Barnard , J. F. G. de Freitas , David A. Forsyth, Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary, Proceedings of the 7th European Conference on Computer Vision-Part IV, p.97-112, May 28-31, 2002
• 4: J. Jeon , V. Lavrenko , R. Manmatha, Automatic image annotation and retrieval using cross-media relevance models, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860459]
• 5: Xin-Jing Wang , Lei Zhang , Feng Jing , Wei-Ying Ma, AnnoSearch: Image Auto-Annotation by Search, Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.1483-1490, June 17-22, 2006 [doi>10.1109/CVPR.2006.58]
• 6: C. Yang, M. Dong and F. Fotouhi. Region Based Image Annotation Through Multiple-Instance Learning. ACM MM, 2004.
• 7: Hua-Jun Zeng , Qi-Cai He , Zheng Chen , Wei-Ying Ma , Jinwen Ma, Learning to cluster web search results, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009030]
• 8: L. Zhang, Y. Hu, M. Li, W. Ma and H. Zhang. Efficient Propagation for Face Annotation in Family Albums. ACM MM, 2002.
• 9: Hakan Ferhatosmanoglu , Ertem Tuncel , Divyakant Agrawal , Amr El Abbadi, Vector approximation based indexing for non-uniform high dimensional data sets, Proceedings of the ninth international conference on Information and knowledge management, p.202-209, November 06-11, 2000, McLean, Virginia, United States [doi>10.1145/354756.354820]
• 10: Aristides Gionis , Piotr Indyk , Rajeev Motwani, Similarity Search in High Dimensions via Hashing, Proceedings of the 25th International Conference on Very Large Data Bases, p.518-529, September 07-10, 1999
• 11: Hakan Ferhatosmanoglu , Ertem Tuncel , Divyakant Agrawal , Amr El Abbadi, Approximate Nearest Neighbor Searching in Multimedia Databases, Proceedings of the 17th International Conference on Data Engineering, p.503-511, April 02-06, 2001
• 12: S. E. Robertson, S. Walker, S. Jones, M. M. Hancock- Beaulieu and M. Gatford. Okapi at TREC-3. TREC-3, 1995.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.8 Scene Analysis
          Subjects: Object recognition

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Image databases

General Terms:
Algorithms, Experimentation, Measurement

Keywords:
automatic image annotation, result clustering, similarity search