Open Access

Combining Low-Level Features for Semantic Extraction in Image Retrieval

EURASIP Journal on Advances in Signal Processing20072007:061423

https://doi.org/10.1155/2007/61423

Received: 9 September 2006

Accepted: 16 April 2007

Published: 17 December 2007

Abstract

An object-oriented approach for semantic-based image retrieval is presented. The goal is to identify key patterns of specific objects in the training data and to use them as object signature. Two important aspects of semantic-based image retrieval are considered: retrieval of images containing a given semantic concept and fusion of different low-level features. The proposed approach splits the image into elementary image blocks to obtain block regions close in shape to the objects of interest. A multiobjective optimization technique is used to find a suitable multidescriptor space in which several low-level image primitives can be fused. The visual primitives are combined according to a concept-specific metric, which is learned from representative blocks or training data. The optimal linear combination of single descriptor metrics is estimated by applying the Pareto archived evolution strategy. An empirical assessment of the proposed technique was conducted to validate its performance with natural images.

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Authors’ Affiliations

(1)
Multimedia and Vision Laboratory, Electronic Engineering Department, Queen Mary University of London

References

  1. Bishop C, Bishop CM: Neural Networks for Pattern Recognition. Oxford University Press, Oxford, Miss, USA; 1995.MATHGoogle Scholar
  2. Chapelle O, Haffner P, Vapnik VN: Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks 1999,10(5):1055-1064. 10.1109/72.788646View ArticleGoogle Scholar
  3. Friedman N, Geiger D, Goldszmidt M: Bayesian network classifiers. Machine Learning 1997,29(2):131-163. 10.1023/A:1007465528199View ArticleMATHGoogle Scholar
  4. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000,22(12):1349-1380. 10.1109/34.895972View ArticleGoogle Scholar
  5. Rui Y, Huang TS, Chang S-F: Image retrieval: current techniques, promising directions, and open issues. Journal of Visual Communication and Image Representation 1999,10(1):39-62. 10.1006/jvci.1999.0413View ArticleGoogle Scholar
  6. Chang S-F, Sikora T, Purl A: Overview of the MPEG-7 standard. IEEE Transactions on Circuits and Systems for Video Technology 2001,11(6):688-695. 10.1109/76.927421View ArticleGoogle Scholar
  7. Mojsilović A: A computational model for color naming and describing color composition of images. IEEE Transactions on Image Processing 2005,14(5):690-699.View ArticleGoogle Scholar
  8. Rui Y, Huang TS, Ortega M, Mehrotra S: Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology 1998,8(5):644-655. 10.1109/76.718510View ArticleGoogle Scholar
  9. Newsam S, Sumengen B, Manjunath BS: Category-based image retrieval. Proceedings of IEEE International Conference on Image Processing (ICIP '01), October 2001, Thessaloniki, Greece 3: 596-599.Google Scholar
  10. Schmid C, Mohr R: Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997,19(5):530-535. 10.1109/34.589215View ArticleGoogle Scholar
  11. Soysal M, Alatan AA: Combining MPEG-7 based visual experts for reaching semantics. Proceedings of the 8th International Workshop on Visual Content Processing and Representation (VLBV '03), September 2003, Madrid, Spain, Lecture Notes in Computer Science 2849: 66-75.View ArticleGoogle Scholar
  12. Djordjevic D, Izquierdo E: An object- and user-driven system for semantic-based image annotation and retrieval. IEEE Transactions on Circuits and Systems for Video Technology 2007,17(3):313-323.View ArticleGoogle Scholar
  13. Steuer RE: Multiple Criteria Optimization: Theory, Computation, and Application. John Wiley & Sons, New York, NY, USA; 1986.MATHGoogle Scholar
  14. Knowles J, Corne D: Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation 2000,8(2):149-172. 10.1162/106365600568167View ArticleGoogle Scholar
  15. Manjunath BS, Ma WY: Texture features for browsing and retrieval of image data. IEEE Transactions on Pattern Analysis and Machine Intelligence 1996,18(8):837-842. 10.1109/34.531803View ArticleGoogle Scholar
  16. Tuceryan M, Jain AK: Texture analysis. In The Handbook of Pattern Recognition and Computer Vision. 2nd edition. World Scientific, River Edge, NJ, USA; 1998:207-248.Google Scholar
  17. Swain MJ, Ballard DH: Color indexing. International Journal of Computer Vision 1991,7(1):11-32. 10.1007/BF00130487View ArticleGoogle Scholar
  18. Zhang Q, Izquierdo E: A multi-feature optimization approach to object-based image classification. Proceedings the 5th International Conference on Image and Video Retrieval (CIVR '06), July 2006, Tempe, Ariz, USA, Lecture Notes in Computer Science 4071: 310-319.View ArticleGoogle Scholar
  19. Knowles J, Corne D: Properties of an adaptive archiving algorithm for storing nondominated vectors. IEEE Transactions on Evolutionary Computation 2003,7(2):100-116. 10.1109/TEVC.2003.810755View ArticleGoogle Scholar
  20. Jaszkiewicz A: Multiple Objective Metaheuristic Algorithms for Combinatorial Optimization. Poznan University of Technology, Poznan, Poland; 2001.MATHGoogle Scholar

Copyright

© Q. Zhang and E. Izquierdo. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.