Skip to main content
SpringerLink
Log in

Region-based image retrieval using shape-adaptive DCT

  • Regular Paper
  • Published:
International Journal of Multimedia Information Retrieval Aims and scope Submit manuscript

A Publisher's Erratum to this article was published on 14 September 2015

Abstract

Content-based image retrieval (CBIR) is the process of searching digital images in a large database based on features, such as color, texture and shape of a given query image. As many images are compressed by transforms, constructing the feature vector directly in transform domain is a very popular topic. Therefore, features can be extracted directly from images in compressed format by using, for example, discrete cosine transform (DCT) for JPEG compressed images. Also, region-based image retrieval (RBIR) has attracted great interest in recent years. This paper proposes a new RBIR approach using shape-adaptive discrete cosine transform (SA-DCT). In this retrieval system, an image has a prior segmentation alpha plane, which is defined exactly as in MPEG-4. Therefore, an image is represented by segmented regions, each of which is associated with a feature vector derived from DCT and SA-DCT coefficients. Users can select any region as the main theme of the query image. The similarity between a query image and any database image is ranked according to a same similarity measure computed from the selected regions between two images. For those images without distinctive objects and scenes, users can still select the whole image as the query condition. The experimental results show that the proposed approach is able to identify main objects and reduce the influence of background in the image, and thus improve the performance of image retrieval in comparison with a conventional CBIR based on DCT.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Thomee B, Lew M (2012) Interactive search in image retrieval: a survey. Int J Multimed Inf Retr 1:71–86

    Article  Google Scholar 

  2. Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40(2):5:1–60

  3. Huang W, Gao Y, Chan KL (2010) A review of region-based image retrieval. J Signal Process Syst 59:143–161

    Article  Google Scholar 

  4. Wang JZ, Li J, Wiederhold G (2001) Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Trans Pattern Anal Mach Intell 23(9):947–963

    Article  Google Scholar 

  5. Agarwal M, Maheshwari R (2012) A trous gradient structure descriptor for content based image retrieval. Int J Multimed Inf Retr 1(2):129–138

    Article  Google Scholar 

  6. Murala S, Maheshwari RP, Balasubramanian R (2012) Directional local extrema patterns: a new descriptor for content based image retrieval. Int J Multimed Inf Retr 1(3):191–203

    Article  Google Scholar 

  7. Bai C, Zhang J, Liu Z, Zhao WL (2014) K-means based histogram using multiresolution feature vectors for color texture database retrieval. Multimed Tools Appl 69(5):1–20

    Google Scholar 

  8. Wang XY, Zhang BB, Yang HY (2014) Content-based image retrieval by integrating color and texture features. Multimed Tools Appl 68(4):545–569

    Article  Google Scholar 

  9. Sun Y, Ozawa S (2005) Hirbir: a hierarchical approach to region-based image retrieval. Multimed Syst 10(6):559–569

    Article  Google Scholar 

  10. Liu Y, Zhang DS, Lu G, Ma WY (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recogn 40(1):262–282

    Article  MATH  Google Scholar 

  11. Liu Y, Zhang DS, Lu G (2008) Region-based image retrieval with high-level semantics using decision tree learning. Pattern Recogn 41(1):2554–2570

    Article  MATH  Google Scholar 

  12. Liu Y, Chen X, Zhang C, Sprague A (2009) Semantic clustering for region-based image retrieval. J Vis Commun Image Represent 20:157–166

    Article  Google Scholar 

  13. Yang X, Cai L (2014) Adaptive region matching for region-based image retrieval by constructing region importance index. IET Comput Vis 8(2):141–151

    Article  Google Scholar 

  14. Jing F, Li M, Zhang H, Zhang B (2004) An efficient and effective region-based image retrieval framework. IEEE Trans Image Process 13(5):699–709

    Article  Google Scholar 

  15. Shokoufandeh A, Keselman Y, Demirci MF, Macrini D, Dickinson S (2012) Many-to-many feature matching in object recognition: a review of three approaches. IET Comput Vis 6(6):500–513

    Article  Google Scholar 

  16. Zou W, Kpalma K, Ronsin J (2012) Semantic image segmentation using region bank. In: Proceedings ICPR’12 (international conference on pattern recognition), pp 922–925

  17. Schneier M, Abdel-Mottaleb M (1996) Exploiting the jpeg compression scheme for image retrieval. IEEE Trans Pattern Anal Mach Intell 18(8):849–853

    Article  Google Scholar 

  18. Eickeler S, Muller S, Rigoll G (2000) Recognition of jpeg compressed face images based on statistical methods. Image Vis Comput 18(4):279–287

    Article  Google Scholar 

  19. Ngo C, Pong T, Chin R (2001) Exploiting image indexing techniques in dct domain. Pattern Recogn 34(9):1841–1845

    Article  MATH  Google Scholar 

  20. Climer S, Bhatia SK (2002) Image database indexing using jpeg coefficients. Pattern Recogn 35(11):2479–2488

    Article  MATH  Google Scholar 

  21. Dabbaghchian S, Ghaemmaghami M, Aghagolzadeh A (2010) Feature extraction using discrete cosine transform and discrimination power analysis with a face recognition technology. Pattern Recogn 43:1431–1440

    Article  MATH  Google Scholar 

  22. Cheng K, Law N, Siu W (2010) Fast extraction of wavelet-based features from jpeg images for joint retrieval with jpeg2000 images. Pattern Recogn 43:3314–3323

    Article  MATH  Google Scholar 

  23. Jiang J, Amstrong A, Feng G (2002) Direct content access and extraction from jpeg compressed images. Pattern Recogn 35(11):2511–2519

    Article  MATH  Google Scholar 

  24. Feng G, Jiang J (2003) Jpeg compressed image retrieval via statistical features. Pattern Recogn 36(4):977–985

    Article  Google Scholar 

  25. Chang C, Chuang J, Hu Y (2004) Retrieving digital images from a jpeg compressed image database. Image and Vis Comput 22(6):471–484

    Article  Google Scholar 

  26. Zhong D, Defee I (2005) Dct histogram optimization for image database retrieval. Pattern Recogn Lett 26(14):2272–2281

    Article  Google Scholar 

  27. Bai C, Kpalma K, Ronsin J (2012) Color textured image retrieval by combining texture and color features. In: Proceedings EUSIPCO’12 (European signal processing conference), pp 170–174

  28. Edmundson D, Schaefer G (2012) Fast jpeg image retrieval using optimised huffman tables. In: Proceedings ICPR’12 (international conference on pattern recognition), vol. IV, pp 3188–3191

  29. Edmundson D, Schaefer G, Celebi M (oct 2012) Robust texture retrieval of compressed images. In: Proceedings ICIP-12 (IEEE international conference on image processing), vol. IV, pp 2421–2424

  30. Zhong D, Defee I (2007) Performance of similarity measures based on histograms of local image feature vectors. Pattern Recogn Lett 28(15):2003–2010

    Article  Google Scholar 

  31. Liu Y, Zhou X, Ma WY (2004) Extraction of texture features from arbitrary-shaped regions for image retrieval. In: Proceedings ICME’04 (international conference on Multimedia and Expo), pp 1891–1894

  32. Zhang D, Islam M, Lu G, Sumana I (2012) Rotation invariant curvelet features for region based image retrieval. Int J Comput Vis 98(2):187–201

    Article  MathSciNet  Google Scholar 

  33. Sikora T, Makai B (1995) Shape-adaptive DCT for generic coding of video. IEEE Trans Circ Syst Video Technol 5(1):59–62

    Article  Google Scholar 

  34. ISO/IEC JTC1/SC29/WG11 (1997) MPEG-4 video verification model version 8.0. MPEG97/N1796

  35. Belloulata K, Belhallouche L, Belalia A, Kpalma K (2014) Region based image retrieval using shape-adaptive dct. In: Proceedings ChinaSIP-14 (2nd IEEE China summit and international conference on signal and information processing), pp 470–474

  36. Jiang J, Feng G (2002) The spatial relationship of dct coefficients between a block and its sub-blocks. IEEE Trans Signal Process 5(11):1160–1169

    Article  Google Scholar 

  37. Bai C, Kpalma K, Ronsin J (2012) A new descriptor based on 2d dct for image retrieval. In: Proceedings VISAPP’12 (international conference on computer vision theory and applications), pp 714–717

  38. Zhong D, Defee I (2008) Face retrieval based on robust local features and statistical-structural learning approach. In: EURASIP journal on advances in signal processing, vol 2008, no. ID 631297, p 12

  39. Ferman A, Tekalp M, Mehrotra R (2002) Robust color histogram descriptors for video segment retrieval and identification. IEEE Trans Circ Syst Video Technol 11(5):497–508

    Google Scholar 

  40. Rubner Y, Tomasi C, Guibas LJ (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput Vis 40:99–121

    Article  MATH  Google Scholar 

  41. Liu Y, Zhang DS, Lu G, Ma WY (2006) Study on texture feature extraction in region-based image retrieval system. In: Proceedings MMM’06 (international multimedia modeling conference), pp 264–271

  42. Chen H, Civanlar M, Haskell B (1994) A block transform coder for arbitrarily-shaped image segments. In: Proceedings ICIP-94 (iEEE international conference on image processing), pp 85–89

  43. Stasinski R, Konrad J (1999) A new class of fast shape-adaptive orthogonal transforms and their application to region-based image compression. IEEE Trans Circ Syst Video Technol 9(1):16–34

    Article  Google Scholar 

  44. Carson C, Belongie S, Greenspan H, Malik J (2002) Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Trans Pattern Anal Mach Intell 24(8):1026–1038

    Article  Google Scholar 

  45. Bresson X, Esedoglu S, Vandergheynst P, Thiran J, Osher S (2007) Fast global minimization of the active contour/snake model. J Math Imag Vis 28(2):151–167

    Article  MathSciNet  Google Scholar 

  46. Zou W, Kpalma K, Ronsin J (2012) Semantic segmentation via sparse coding over hierarchical regions. In: Proceedings ICIP-12 (IEEE international conference on image processing), pp 2577–2580

  47. Zou W, Kpalma K, Ronsin J (2013) Automatic foreground extraction via joint crf and online learning. Electron Lett 49(18):1140–1142

    Article  Google Scholar 

  48. Sikora T (1995) Low complexity shape-adaptive DCT for coding of arbitrarily shaped image segments. Signal Process Image Commun 7(4–6):381–395

    Article  Google Scholar 

  49. Gilge M, Engelhardt T, Mehlan R (1989) Coding of arbitrarily shaped image segments based on a generalized orthogonal transform. Signal Process Image Commun 1(2):153–180

    Article  Google Scholar 

  50. Hsu H, Lee K, Chang N, Chang T (2008) Architecture design of shape-adaptive discrete cosine transform and its inverse for mpeg-4 video coding. IEEE Trans Circ Syst Video Technol 18(3):375–386

    Article  Google Scholar 

  51. Belloulata K, Konrad J (2002) Fractal image compression with region-based functionality. IEEE Trans Image Process 11(4):351–362

    Article  Google Scholar 

  52. Belloulata K, Belalia A, Zhu S (2014) Object-based stereo video compression using fractals and shape-adaptive dct. Int J Electron Commun 68(7):687–697

    Article  Google Scholar 

  53. Kauff P, Schüür K (1998) Shape-adaptive DCT with block-based DC separation and \(\Delta \)DC correction. IEEE Trans Circ Syst Video Technol 8(3):237–242

    Article  Google Scholar 

  54. http://wang.ist.psu.edu/docs/related.shtml/test1.tar. Accessed Jan 2013

  55. http://www.vision.caltech.edu/ImDatasets/Caltech256/. Accessed March 2014

  56. http://www.anefian.com/research/face_reco.htm. Georgia tech, GTF database. Accessed March 2012

  57. Griffin G, Holub A, Perona P (2007) Caltech-256 object category dataset pp 1–20. http://resolver.caltech.edu/CaltechAUTHORS:CNS-TR-2007-001. Accessed Apr 2007

  58. Manipoonchelvi P, Muneeswaran K (2014) Significant region-based image retrieval. In: Signal image and video processing, no. 6, Springer, New York, pp 1–8

  59. Murala S, Wu QM (2014) Expert content-based image retrieval system using robust local patterns. J Vis Commun Image Represent 25:1324–1334

    Article  Google Scholar 

  60. Yanping D, Wang JZ (2001) A scalable integrated region-based image retrieval system. In: Proceedings ICIP-01 (IEEE international conference on image processing), vol. I, pp 22–25

  61. Bolle RM, Pankanti S, Ratha NK (2000) Evaluation techniques for biometrics-based authentication systems (frr). In: Proceedings ICPR’00 (international conference on pattern recognition, vol. II, pp 831–837

Download references

Acknowledgments

This work is currently supported by the Partenariat Hubert Curien PHC-TASSILI under Grant No. 12MDU864. The authors thank for their financial supports. We would like to thank the editor and anonymous reviewers for insightful comments and helpful suggestions to improve the quality of the paper.

Author information

Authors and Affiliations

  1. Electronics Department, Faculty of Engineering, University of Djillali Liabes, BP 89, Sidi bel Abbès, Algeria

    Amina Belalia & Kamel Belloulata

  2. UMR 6164, IETR, UEB INSA, 35708, Rennes, France

    Kidiyo Kpalma

Authors
  1. Amina Belalia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamel Belloulata
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kidiyo Kpalma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kamel Belloulata.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Belalia, A., Belloulata, K. & Kpalma, K. Region-based image retrieval using shape-adaptive DCT. Int J Multimed Info Retr 4, 261–276 (2015). https://doi.org/10.1007/s13735-015-0084-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13735-015-0084-1

Keywords

Search

Navigation