Automatic image annotation and retrieval using cross-media relevance models

ABSTRACT

Libraries have traditionally used manual image annotation for indexing and then later retrieving their image collections. However, manual image annotation is an expensive and labor intensive procedure and hence there has been great interest in coming up with automatic ways to retrieve images based on content. Here, we propose an automatic approach to annotating and retrieving images based on a training set of images. We assume that regions in an image can be described using a small vocabulary of blobs. Blobs are generated from image features using clustering. Given a training set of images with annotations, we show that probabilistic models allow us to predict the probability of generating a word given the blobs in an image. This may be used to automatically annotate and retrieve images given a word as a query. We show that relevance models allow us to derive these probabilities in a natural way. Experiments show that the annotation performance of this cross-media relevance model is almost six times as good (in terms of mean precision) than a model based on word-blob co-occurrence model and twice as good as a state of the art model derived from machine translation. Our approach shows the usefulness of using formal information retrieval models for the task of image annotation and retrieval.

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: K. Barnard and D. Forsyth. Learning the semantics of words and pictures. In International Conference on Computer Vision, Vol.2, pages 408--415, 2001.
• 3: David M. Blei , Michael I. Jordan, Modeling annotated data, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860460]
• 4: Adam Berger , John Lafferty, Information retrieval as statistical translation, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.222-229, August 15-19, 1999, Berkeley, California, United States [doi>10.1145/312624.312681]
• 5: Peter F. Brown , Vincent J. Della Pietra , Stephen A. Della Pietra , Robert L. Mercer, The mathematics of statistical machine translation: parameter estimation, Computational Linguistics, v.19 n.2, June 1993
• 6: W. B. Croft. Combining Approaches to Information Retrieval, in Advances in Information Retrieval ed. W. B. Croft, Kluwer Academic Publishers, Boston, MA.
• 7: Chad Carson , Megan Thomas , Serge Belongie , Joseph M. Hellerstein , Jitendra Malik, Blobworld: A System for Region-Based Image Indexing and Retrieval, Proceedings of the Third International Conference on Visual Information and Information Systems, p.509-516, June 02-04, 1999
• 8: Madirakshi Das , R. Manmatha , Edward M. Riseman, Indexing Flower Patent Images Using Domain Knowledge, IEEE Intelligent Systems, v.14 n.5, p.24-33, September 1999 [doi>10.1109/5254.796084]
• 9: 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
• 10: David A. Forsyth , Jean Ponce, Computer Vision: A Modern Approach, Prentice Hall Professional Technical Reference, 2002
• 11: D. Hiemstra Using Language Models for Information Retrieval. PhD dissertation, University of Twente, Enschede, The Netherlands, 2001.
• 12: Jay M. Ponte , W. Bruce Croft, A language modeling approach to information retrieval, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.275-281, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.291008]
• 13: Victor Lavrenko , W. Bruce Croft, Relevance based language models, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.120-127, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383972]
• 14: Victor Lavrenko , Martin Choquette , W. Bruce Croft, Cross-lingual relevance models, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564408]
• 15: Y. Mori, H. Takahashi, and R. Oka. Image-to-word transformation based on dividing and vector quantizing images with words. In MISRM'99 First International Workshop on Multimedia Intelligent Storage and Retrieval Management, 1999.
• 16: R. W. Picard , T. P. Minka, Vision texture for annotation, Multimedia Systems, v.3 n.1, p.3-14, Feb. 1995 [doi>10.1007/BF01236575]
• 17: Jianbo Shi , Jitendra Malik, Normalized Cuts and Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.8, p.888-905, August 2000 [doi>10.1109/34.868688]
• 18: John Lafferty , Chengxiang Zhai, Document language models, query models, and risk minimization for information retrieval, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.111-119, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383970]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

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

General Terms:
Algorithms, Experimentation, Measurement

Keywords:
image annotation, image retrieval, relevance models