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Combining Low-Level Features for Semantic Extraction in Image Retrieval

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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.

References

  1. 1.

    Bishop C, Bishop CM: Neural Networks for Pattern Recognition. Oxford University Press, Oxford, Miss, USA; 1995.

  2. 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. 10.1109/72.788646

  3. 3.

    Friedman N, Geiger D, Goldszmidt M: Bayesian network classifiers. Machine Learning 1997,29(2):131-163. 10.1023/A:1007465528199

  4. 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. 10.1109/34.895972

  5. 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.1999.0413

  6. 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. 10.1109/76.927421

  7. 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.

  8. 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. 10.1109/76.718510

  9. 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.

  10. 10.

    Schmid C, Mohr R: Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997,19(5):530-535. 10.1109/34.589215

  11. 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.

  12. 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.

  13. 13.

    Steuer RE: Multiple Criteria Optimization: Theory, Computation, and Application. John Wiley & Sons, New York, NY, USA; 1986.

  14. 14.

    Knowles J, Corne D: Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation 2000,8(2):149-172. 10.1162/106365600568167

  15. 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. 10.1109/34.531803

  16. 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.

  17. 17.

    Swain MJ, Ballard DH: Color indexing. International Journal of Computer Vision 1991,7(1):11-32. 10.1007/BF00130487

  18. 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.

  19. 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.2003.810755

  20. 20.

    Jaszkiewicz A: Multiple Objective Metaheuristic Algorithms for Combinatorial Optimization. Poznan University of Technology, Poznan, Poland; 2001.

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Correspondence to Q. Zhang.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Keywords

  • Training Data
  • Image Retrieval
  • Multiobjective Optimization
  • Natural Image
  • Semantic Concept