Skip to main content
Springer Nature Link
Log in

A fast and effective image retrieval scheme using color-, texture-, and shape-based histograms

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

The rapid growth of digital image collections has prompted the need for development of software tools that facilitate efficient searching and retrieval of images from large image databases. Towards this goal, we propose a content-based image retrieval scheme for retrieval of images via their color, texture, and shape features. Using three specialized histograms (i.e. color, wavelet, and edge histograms), we show that a more accurate representation of the underlying distribution of the image features improves the retrieval quality. Furthermore, in an attempt to better represent the user’s information needs, our system provides an interactive search mechanism through the user interface. Users searching through the database can select the visual features and adjust the associated weights according to the aspects they wish to emphasize. The proposed histogram-based scheme has been thoroughly evaluated using two general-purpose image datasets consisting of 1000 and 3000 images, respectively. Experimental results show that this scheme not only improves the effectiveness of the CBIR system, but also improves the efficiency of the overall process.

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

Similar content being viewed by others

Notes

  1. The response time is displayed in the GUI every time the user clicks the ‘Search Similar Images’ button.

References

  1. Agarwal M, Maheshwari R (2012) Á Trous gradient structure descriptor for content based image retrieval. International Journal of Multimedia Information Retrieval 1(2):129–138

    Article  Google Scholar 

  2. Androutsos D, Plataniotis KN, Venetsanopoulos AN (1999) A novel vector-based approach to color image retrieval using a vector angular-based distance measure. Comput Vis Image Underst 75(1-2):46–58

    Article  Google Scholar 

  3. Babu CR, Reddy ES, Rao BP (2015) Age group classification of facial images using rank based edge texture unit (retu). In: Information Systems Design and Intelligent Applications. Springer, pp 483–496

  4. Carson C, Thomas M, Belongie S, Hellerstein JM, Malik J (1999) Blobworld: A system for region-based image indexing and retrieval. In: Visual Information and Information Systems. Springer, pp 509–517

  5. Chun YD, Seo SY, Kim NC (2003) Image retrieval using BDIP and BVLC moments. IEEE Trans Circuits Syst Video Technol 13(9):951–957

    Article  Google Scholar 

  6. Chun YD, Kim NC, Jang IH (2008) Content-based image retrieval using multiresolution color and texture features. IEEE Trans Multimedia 10(6):1073–1084

    Article  Google Scholar 

  7. Cox IJ, Miller ML, Minka TP, Papathomas TV, Yianilos PN (2000) The bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments. IEEE Trans Image Process 9(1):20–37

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Deng Y, Manjunath B (1999) An efficient low-dimensional color indexing scheme for region-based image retrieval. In: 1999 IEEE International conference on acoustics, speech, and signal processing, vol 6. IEEE, pp 3017–3020

  10. Faloutsos C, Barber R, Flickner M, Hafner J, Niblack W, Petkovic D, Equitz W (1994) Efficient and effective querying by image content. J Intell Inf Syst 3(3-4):231–262

    Article  Google Scholar 

  11. Flickner M, Sawhney H, Niblack W, Ashley J, Huang Q, Dom B, Gorkani M, Hafner J, Lee D, Petkovic D, Steele D, Yanker P (1995) Query by image and video content: The QBIC system. Computer 28(9):23–32

    Article  Google Scholar 

  12. Fonseca MJ, Ferreira A, Jorge JA (2006) Generic shape classification for retrieval. In: Graphics Recognition. Ten Years Review and Future Perspectives, vol 3926. Springer, pp 291–299

  13. Gevers T, Smeulders AWM (2000) PicToSeek: Combining color and shape invariant features for image retrieval. IEEE Trans Image Process 9(1):102–119

    Article  Google Scholar 

  14. Gudivada VN, Raghavan VV (1995) Content based image retrieval systems. Computer 28(9):18–22

    Article  Google Scholar 

  15. Guldogan E, Gabbouj M (2009) Dynamic feature weights with relevance feedback in content-based image retrieval. In: 24th International Symposium on Computer and Information Sciences. IEEE, pp 56–59

  16. Han J, Ma KK (2007) Rotation-invariant and scale-invariant gabor features for texture image retrieval. Image Vision Comput 25(9):1474–1481

    Article  Google Scholar 

  17. Han JW, Guo L (2003) A shape-based image retrieval method using salient edges. Signal processing: Image communication 18(2):141–156

    Google Scholar 

  18. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC 3(6):610–621

    Article  Google Scholar 

  19. Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8(2):179– 187

    Article  MATH  Google Scholar 

  20. Huang J, Kumar SR, Mitra M, Zhu WJ, Zabih R (1997) Image indexing using color correlograms. In: 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp 762–768

  21. Huiskes MJ, Lew MS (2008) The MIR Flickr retrieval evaluation. In: Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval. ACM, pp 39–43

  22. Jain AK, Vailaya A (1996) Image retrieval using color and shape. Pattern Recogn 29(8):1233–1244

    Article  Google Scholar 

  23. Jégou H, Douze M, Schmid C, Pérez P (2010) Aggregating local descriptors into a compact image representation. IEEE

  24. Jeong S, Won CS, Gray RM (2004) Image retrieval using color histograms generated by gauss mixture vector quantization. Comput Vis Image Underst 94 (1-3):44–66

    Article  Google Scholar 

  25. Jiawei H, Kamber M (2006) Data mining: concepts and techniques, 2nd edn. Morgan Kaufmann, USA

    MATH  Google Scholar 

  26. Kokare M, Biswas PK, Chatterji BN (2007) Texture image retrieval using rotated wavelet filters. Pattern Recogn Lett 28(10):1240–1249

    Article  Google Scholar 

  27. Li X (2003) Image retrieval based on perceptive weighted color blocks. Pattern Recogn Lett 24(12):1935–1941

    Article  Google Scholar 

  28. Liapis S, Tziritas G (2004) Color and texture image retrieval using chromaticity histograms and wavelet frames. IEEE Trans Multimedia 6(5):676–686

    Article  Google Scholar 

  29. Lin CH, Chen RT, Chan YK (2009) A smart content-based image retrieval system based on color and texture feature. Image Vision Comput 27(6):658–665

    Article  Google Scholar 

  30. Liu F, Picard RW (1996) Periodicity, directionality, and randomness: Wold features for image modeling and retrieval. IEEE Trans Pattern Anal Mach Intell 18 (7):722–733

    Article  Google Scholar 

  31. Liu Y, Zhang D, 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 

  32. Llorente A, Manmatha R, Rüger S (2010) Image retrieval using markov random fields and global image features. In: Proceedings of the ACM International Conference on Image and Video Retrieval. ACM, pp 243–250

  33. Long F, Zhang H, Feng DD (2003) Fundamentals of content-based image retrieval. In: Multimedia Information Retrieval and Management. Springer, pp 1–26

  34. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  35. Lu TC, Chang CC (2007) Color image retrieval technique based on color features and image bitmap. Inf Process Manag 43(2):461–472

    Article  MathSciNet  Google Scholar 

  36. Ma WY, Manjunath BS (1999) NeTra: A toolbox for navigating large image databases. Multimedia Syst 7(3):184–198

    Article  Google Scholar 

  37. Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842

    Article  Google Scholar 

  38. Manjunath BS, Ohm JR, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst Video Technol 11(6):703–715

    Article  Google Scholar 

  39. Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge university press, New York

    Book  MATH  Google Scholar 

  40. Matias Y, Vitter JS, Wang M (1998) Wavelet-based histograms for selectivity estimation. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data. ACM, pp 448– 459

  41. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175

    Article  MATH  Google Scholar 

  42. Ooi BC, Tan KL, Chua TS, Hsu W (1998) Fast image retrieval using color-spatial information. VLDB J 7(2):115–128

    Article  Google Scholar 

  43. Pass G, Zabih R, Miller J (1996) Comparing images using color coherence vectors. In: Proceedings of the Fourth ACM International Conference on Multimedia. ACM, pp 65–73

  44. Pentland A, Picard RW, Sclaroff S (1996) Photobook: Content-based manipulation of image databases. Int J Comput Vision 18(3):233–254

    Article  Google Scholar 

  45. Persoon E, Fu KS (1977) Shape discrimination using fourier descriptors. IEEE Trans Syst Man Cybern 7(3):170–179

    Article  MathSciNet  Google Scholar 

  46. Pi MH, Tong CS, Choy SK, Zhang H (2006) A fast and effective model for wavelet subband histograms and its application in texture image retrieval. IEEE Trans Image Process 15(10):3078–3088

    Article  Google Scholar 

  47. Puzicha J, Hofmann T, Buhmann JM (1997) Non-parametric similarity measures for unsupervised texture segmentation and image retrieval. In: 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, pp 267–272

  48. Rao LK, Rao DV (2015) Local quantized extrema patterns for content-based natural and texture image retrieval. HCIS 5(1):1–24

    Google Scholar 

  49. Razavian AS, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding baseline for recognition. In: 2014 IEEE Conference on computer vision and pattern recognition workshops(CVPRW). IEEE, pp 512–519

  50. Robinson GS (1977) Edge detection by compass gradient masks. Comput Graphics Image Process 6(5):492–501

    Article  Google Scholar 

  51. 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(5):644–655

    Article  Google Scholar 

  52. Rui Y, Huang TS, Chang SF (1999) Image retrieval: Current techniques, promising directions, and open issues. J Vis Commun Image R 10(1):39–62

    Article  Google Scholar 

  53. Sikora T (2001) The mpeg-7 visual standard for content description an overview. IEEE Trans Circuits Syst Video Technol 11(6):696–702

    Article  MathSciNet  Google Scholar 

  54. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380

    Article  Google Scholar 

  55. Smith AR (1978) Color gamut transform pairs. In: Proceedings of the 5th Annual Conference on Computer Graphics and Interactive Techniques, ACM, SIGGRAPH ’78, pp 12–19

  56. Smith J, Chang SF (1996a) Automated binary texture feature sets for image retrieval. In: 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol 4. IEEE, pp 2239– 2242

  57. Smith JR, Chang SF, 1996b VisualSEEk: a fully automated content-based image query system. In: Proceedings of the 4th ACM international conference on Multimedia. ACM, pp 87–98

  58. Stricker MA, Orengo M (1995) Similarity of color images Proceedings of the SPIE 2420, storage and retrieval for image and video databases, vol III, pp 381–392

  59. Subrahmanyam M, Wu QJ, Maheshwari R, Balasubramanian R (2013) Modified color motif co-occurrence matrix for image indexing and retrieval. Comput Electr Eng 39(3):762–774

    Article  Google Scholar 

  60. Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vision 7(1):11–32

    Article  Google Scholar 

  61. Veltkamp RC, Tanase M (2002) A survey of content-based image retrieval systems. In: Content-Based Image and Video Retrieval, vol 21. Springer, USA, pp 47–101

  62. Wan X, Kuo CCJ (1996) Color distribution analysis and quantization for image retrieval Proceedings of the SPIE 2670, storage and retrieval for still image and video databases, vol IV, pp 8–16

  63. Wang JZ, Wiederhold G, Firschein O, Wei SX (1998) Content-based image indexing and searching using daubechies’ wavelets. Int J Digit Libr 1(4):311–328

    Article  Google Scholar 

  64. 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 

  65. Wang XY, Yu YJ, Yang HY (2011) An effective image retrieval scheme using color, texture and shape features. Comp Stand Inter 33(1):59–68

    Article  Google Scholar 

  66. Wei CH, Li Y, Chau WY, Li CT (2009) Trademark image retrieval using synthetic features for describing global shape and interior structure. Pattern Recogn 42 (3):386–394

    Article  MATH  Google Scholar 

  67. Wu Y, Zhang A (2002) A feature re-weighting approach for relevance feedback in image retrieval. In: 2002 International Conference on Image Processing, vol 2. IEEE, pp II–581–II–584

  68. Xu X, Lee DJ, Antani S, Long LR (2008) A spine X-ray image retrieval system using partial shape matching. IEEE Trans Inf Technol Biomed 12(1):100–108

    Article  Google Scholar 

  69. Yoo HW, Park HS, Jang DS (2005) Expert system for color image retrieval. Expert Syst Appl 28(2):347–357

    Article  Google Scholar 

  70. Zhang S, Tianm Q, Hua G, Huang Q, Li S (2009) Descriptive visual words and visual phrases for image applications. In: Proceedings of the 17th ACM international conference on Multimedia. ACM, pp 75–84

Download references

Author information

Authors and Affiliations

  1. I.K. Gujral Punjab Technical University, Kapurthala, India

    Amandeep Khokher

  2. CGC Technical Campus, Jhanjeri, India

    Rajneesh Talwar

Authors
  1. Amandeep Khokher
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Rajneesh Talwar
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Amandeep Khokher.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khokher, A., Talwar, R. A fast and effective image retrieval scheme using color-, texture-, and shape-based histograms. Multimed Tools Appl 76, 21787–21809 (2017). https://doi.org/10.1007/s11042-016-4096-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4096-5

Keywords

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy