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Extracting semantic concepts from images: a decisive feature pattern mining approach

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Abstract

One major challenge in the content-based image retrieval (CBIR) and computer vision research is to bridge the so-called “semantic gap” between low-level visual features and high-level semantic concepts, that is, extracting semantic concepts from a large database of images effectively. In this paper, we tackle the problem by mining the decisive feature patterns (DFPs). Intuitively, a decisive feature pattern is a combination of low-level feature values that are unique and significant for describing a semantic concept. Interesting algorithms are developed to mine the decisive feature patterns and construct a rule base to automatically recognize semantic concepts in images. A systematic performance study on large image databases containing many semantic concepts shows that our method is more effective than some previously proposed methods. Importantly, our method can be generally applied to any domain of semantic concepts and low-level features.

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References

  1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 1994 International Conference on Very Large Data Bases (VLDB'94), pp. 487–499. Santiago, Chile (1994)

  2. Barnard, K., Forsyth, D.: Learning the semantics of words and pictures. Proc. ICCV 2, 408–415 (2001)

    Google Scholar 

  3. Benitez, A., Chang, S.-F.: Semantic knowledge construction from annotated image collection. Proc. IEEE Int. Conf. Multimedia (2002)

  4. Brandt, S., Laaksonen, J., Oja, E.: Statistical shape features in content-based image retrieval. Proc. ICPR (2000)

  5. Dong, G., Zhang, X., Wong, L., Li, J.: Caep: classification by aggregating emerging patterns. Discov. Sci. (1999)

  6. Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Machine Learning: Proceedings of the 12th International Conference, pp. 194–202 (1995)

  7. Fayyad, U., Irani, K.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the 13th IJCAI, pp. 1022–1027 (1993)

  8. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD International Conference on Management of Data (2000)

  9. Joachims, T.: Making Large-Scale svm Learning Practical. Advances in Kernel Methods – Support Vector Learning. MIT Press, Cambridge, MA (1999)

    Google Scholar 

  10. Kohavi, R., Sahami, M.: Error-based and entropy-based discretization of continuous features. ACM KDD (1996)

  11. Li, B., Goh, K., Chang, E.: Confidence-based dynamic ensemble for image annotation and semantics discovery. In: Proceedings of the ACM Multimedia 2003

  12. Ma, W.Y., Zhang, H.: Handbook of multimedia computing. In: Chapter of Content-Based Image Indexing and Retrieval, pp. 19–20. Routledge, London (1998)

    Google Scholar 

  13. Pillai, S., Papoulis, A.: Probability, Random Variables, and Stochastic Process. McGraw-Hill Higher Education, New York (2002)

    Google Scholar 

  14. Song, Y., Wang, W., Zhang, A.: Automatic annotation and retrieval of images. In: Proceedings of the Sixth IFIP Working Conference on Vis. pp. 267–280 Database Syst. (2002)

  15. Song, Y., Zhang, A.: Analyzing Scenery Images by Monotonic Tree. ACM Multimedia Systems, Special Issue on CBIR (2003)

  16. Wang, J.Z.: Integrated Region-Based Image Retrieval. Kluwer, Dordrecht (2001)

  17. Wang, J.Z., Li, J.: Learning-based linguistic indexing of pictures with 2-d mhmms. In: Proceedings of the ACM Multimedia (2002)

  18. Wang, W., Song, Y., Zhang, A.: Semantics-based image retrieval by region saliency. In: Proceedings of the International Conference for Image and Video Retrieval pp. 29–37 (2002)

  19. Wang, W., Song, Y., Zhang, A.: Semantics retrieval by content and context of image regions. In: Proceedings of the 15th International Conference on Vision Interface (VI'2002), pp. 17–24. Calgary, Canada (2002)

  20. Zhang, H., Su, Z.: Relevance feedback in cbir. In: Proceedings of the Sixth IFIP Working Conf. on Vis. Database Syst. pp. 21–35 (2002)

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Authors and Affiliations

  1. Motorola Labs, Multimedia Research Lab, 1295 East Algonquin Road, Schaumburg, IL, 60196, USA

    Wei Wang

  2. Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY, 14260, USA

    Aidong Zhang

Authors
  1. Wei Wang
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  2. Aidong Zhang
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Correspondence to Wei Wang.

Additional information

Wei Wang received his Ph.D. degree in Computing Science and Engineering from the State University of New York (SUNY) at Buffalo in 2004, under Dr. Aidong Zhang's supervision. He received the B.Eng. in Electrical Engineering from Xi'an Jiaotong University, China in 1995 and the M.Eng. in Computer Engineering from National University of Singapore in 2000, respectively. He joined Motorola Inc. in 2004, where he is currently a senior research engineer in Multimedia Research Lab, Motorola Applications Research Center. His research interests can be summarized as developing novel techniques for multimedia data analysis applications. He is particularly interested in multimedia information retrieval, multimedia mining and association, multimedia database systems, multimedia processing and pattern recognition. He has published 15 research papers in refereed journals, conferences, and workshops, has served in the organization committees and the program committees of IADIS International Conference e-Society 2005 and 2006, and has been a reviewer for some leading academic journals and conferences. In 2005, his research prototype of “seamless content consumption” was awarded the “most innovative research concept of the year” from the Motorola Applications Research Center.

Dr. Aidong Zhang received her Ph.D. degree in computer science from Purdue University, West Lafayette, Indiana, in 1994. She was an assistant professor from 1994 to 1999, an associate professor from 1999 to 2002, and has been a professor since 2002 in the Department of Computer Science and Engineering at the State University of New York at Buffalo. Her research interests include bioinformatics, data mining, multimedia systems, content-based image retrieval, and database systems. She has authored over 150 research publications in these areas. Dr. Zhang's research has been funded by NSF, NIH, NIMA, and Xerox. Dr. Zhang serves on the editorial boards of International Journal of Bioinformatics Research and Applications (IJBRA), ACMMultimedia Systems, the International Journal of Multimedia Tools and Applications, and International Journal of Distributed and Parallel Databases. She was the editor for ACM SIGMOD DiSC (Digital Symposium Collection) from 2001 to 2003. She was co-chair of the technical program committee for ACM Multimedia 2001. She has also served on various conference program committees. Dr. Zhang is a recipient of the National Science Foundation CAREER Award and SUNY Chancellor's Research Recognition Award.

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Wang, W., Zhang, A. Extracting semantic concepts from images: a decisive feature pattern mining approach. Multimedia Systems 11, 352–366 (2006). https://doi.org/10.1007/s00530-006-0029-x

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