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Independent query refinement and feature re-weighting using positive and negative examples for content-based image retrieval

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Abstract

Query refinement and feature re-weighting are the two core techniques underlying the relevance feedback of content-based image retrieval. Most existing relevance feedback mechanisms generally model the user’s query target with a single query point and weight each extracted feature with a single importance factor. A designed estimation procedure then estimates the best query point and all importance factors by optimizing a formulated criterion which measures the goodness of the estimation. This formulated criterion simultaneously encapsulates all positive and negative examples supplied from the user’s feedback. Under such formulation, the positive and negative examples may contribute contradictorily to the criterion and sometimes may introduce higher difficulty in attaining a good estimation. In this paper, we propose a different statistical formulation to estimate independently two pairs of query points and feature weights from the positive examples and negative examples, respectively. These two pairs then define the likelihood ratio, a criterion term used to rank the relevance of all database images. This approach simplifies the criterion formulation and also avoids the mutual impeditive influence between positive examples and negative examples. The experimental results demonstrate that the proposed approach outperforms some other related approaches.

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References

  1. Ahmad I, Grosky WI (2003) Indexing and retrieval of images by spatial constraints. J Vis Commun Image Represent 14:291–320 Sep

    Article  Google Scholar 

  2. Ashley J, Flickner M, Hafner J, Lee D, Niblack W, Petkovic D (1995) Query by Image and Video Content: The QBIC System. IEEE Computer 28:23–32

    Google Scholar 

  3. Chevalier F, Domenger JP, Benois-Pineau J, Delest M (2007) Retrieval of objects in video by similarity based on graph matching. Pattern Recogn Lett 28:939–949 Jun 1

    Article  Google Scholar 

  4. Dai SY, Zhang YJ (2005) Unbalanced region matching based on two-level description for image retrieval. Pattern Recogn Lett 26:565–580 Apr

    Article  Google Scholar 

  5. Doulamis N, Doulamis A (2006) Evaluation of relevance feedback schemes in content-based in retrieval systems. Signal Process, Image Commun 21:334–357 Apr

    Article  Google Scholar 

  6. Duda RO, Hart PE, Stork DG (2000) Pattern classification. Wiley, New York

    Google Scholar 

  7. Gagaudakis G, Rosin PL (2002) Incorporating shape into histograms for CBIR. Pattern Recogn 35:81–91 Jan

    Article  MATH  Google Scholar 

  8. Giacinto G, Roli F (2005) Instance-based relevance feedback for image retrieval. In Saul LK, Weiss Y, Bottou L (eds) Advances in neural information processing systems, vol 17. MIT, Cambridge, MA, pp 489–496

    Google Scholar 

  9. Greenspan H, Dvir G, Rubner Y (2004) Context-dependent segmentation and matching in image databases. Comput Vis Image Underst 93:86–109 Jan

    Article  Google Scholar 

  10. Hampapur A, Gupta A, Horowitz B, Shu C-F, Fuller C, Bach JR, Gorkani M, Jain RC (1997) Virage video engine. Proc SPIE: Storage and Retrieval for Image and Video Databases 3022:188–198

    Google Scholar 

  11. Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory IT-8:179–187

    Google Scholar 

  12. Ishikawa Y, Subramanya R, Faloutsos C (1998) Mindreader: query databases through multiple examples. In: Proceedings of the VLDB, New York, pp 218–227

  13. Kherfi ML, Ziou D, Bernardi A (2003) Combining positive and negative examples in relevance feedback for content-based image retrieval. J Vis Commun Image Represent 14:428–457 Dec

    Article  Google Scholar 

  14. Kim CR, Chung CW (2003) A multi-step approach for partial similarity search in large image data using histogram intersection. Inf Softw Technol 45:203–215 Mar 15

    Article  Google Scholar 

  15. Kim NW, Kim TY, Choi JS (2005) Edge-based spatial descriptor for content-based image retrieval. Image and Video Retrieval, Proceedings 3568:454–464

    Google Scholar 

  16. Lee DH, Kim HJ (2001) A fast content-based indexing and retrieval technique by the shape information in large image database. J Syst Softw 56:165–182 Mar 1

    Article  Google Scholar 

  17. Lew MS (2000) Next-generation web searches for visual content. IEEE Comput 33:46–53

    Google Scholar 

  18. Lin HC, Chiu CY, Yang SN (2003) Finding textures by textual descriptions, and relevance feedbacks. Pattern Recogn Lett 24:2255–2267 Oct

    Article  MATH  Google Scholar 

  19. Liu Y, Zhang DS, Lu GJ, Ma WY (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recogn 40:262–282 Jan

    Article  MATH  Google Scholar 

  20. Nascimento MA, Sridhar V, Li XB (2003) Effective and efficient region-based image retrieval. J Vis Lang Comput 14:151–179 Apr

    Article  Google Scholar 

  21. Niblack W, Barber R, Equitz W, Flickner MD, Glasman EH, Petkovic D, Yanker P, Faloutsos C, Taubin G (1993) QBIC project: querying images by content, using color, texture, and shape. Proc SPIE: Storage and Retrieval for Image and Video Databases 1908:173–187

    Google Scholar 

  22. Ozer IB, Wolf W, Akansu AN (2002) A graph-based object description for information retrieval in digital image and video libraries. J Vis Commun Image Represent 13:425–459 Dec

    Article  Google Scholar 

  23. Qi XJ, Han YT (2005) A novel fusion approach to content-based image retrieval. Pattern Recogn 38:2449–2465 Dec

    Article  Google Scholar 

  24. Rocchio JJ Jr (1971) Relevance feed back in information retrieval. In: Salton G (ed) SMART retrieval system—experiments in automatic document processing. Prentice Hall, New Jersey, pp 313–323

    Google Scholar 

  25. Rui Y, Huang TS, Mehrotra S (1997) Content-based image retrieval with relevance feedback in MARS. In: Proceedings of the IEEE International Conference on Image Processing, pp 815–818

  26. Rui Y, Huang TS, Ortega M, Mehrotra S (1998) Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Trans Circuits Syst Video Technol 8:644–655 Sep

    Article  Google Scholar 

  27. Seo KK (2007) An application of one-class support vector machines in content-based image retrieval. Expert Syst Appl 33:491–498 Aug

    Article  Google Scholar 

  28. Su Z, Zhang HJ, Li S, Ma SP (2003) Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning. IEEE Trans Image Process 12:924–937 Aug

    Article  Google Scholar 

  29. Wu ST, Rahman MKM, Chow TWS (2005) Content-based image retrieval using growing hierarchical self-organizing quadtree map. Pattern Recogn 38:707–722 May

    Article  MATH  Google Scholar 

  30. Zhang DS, Lu GJ (2005) Study and evaluation of different Fourier methods for image retrieval. Image Vis Comput 23:33–49 Jan 1

    Article  MATH  MathSciNet  Google Scholar 

  31. Zhou XS, Huang TS (2002) Relevance feedback in content-based image retrieval: some recent advances. Inf Sci 148:129–137 Dec

    Article  MATH  MathSciNet  Google Scholar 

  32. Zhou XS, Huang TS (2003) Relevance feedback in image retrieval: A comprehensive review. Multimedia Syst 8:536–544 Apr

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Dong Hwa University, Shoufeng, Hualien, Taiwan, 974

    Cheng-Chin Chiang, Jyun-Yue Wu, Mau-Tsuen Yang & Wen-Kai Tai

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  1. Cheng-Chin Chiang
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  2. Jyun-Yue Wu
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  3. Mau-Tsuen Yang
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  4. Wen-Kai Tai
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Correspondence to Cheng-Chin Chiang.

Additional information

This work is partially supported by the NSC of Taiwan under grant 92-2213-E-259-017-.

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Chiang, CC., Wu, JY., Yang, MT. et al. Independent query refinement and feature re-weighting using positive and negative examples for content-based image retrieval. Multimed Tools Appl 41, 27–53 (2009). https://doi.org/10.1007/s11042-008-0218-z

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