Skip to main content
SpringerLink
Log in

CoMo: a scale and rotation invariant compact composite moment-based descriptor for image retrieval

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

Abstract

Low level features play a significant role in image retrieval. Image moments can effectively represent global information of image content while being invariant under translation, rotation, and scaling. This paper presents CoMo: a moment based composite and compact low-level descriptor that can be used effectively for image retrieval and robot vision tasks. The proposed descriptor is evaluated by employing the Bag-of-Visual-Words representation over various well-known benchmarking image databases. The findings from the experimental evaluation provide strong evidence of high and competitive retrieval performance against various state-of-the-art local descriptors.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Notes

  1. http://www.lire-project.net/

References

  1. Aslan S, Akgül CB, Sankur B, Tunali ET (2017) Exploring visual dictionaries: a model driven perspective. J Vis Commun Image Represent 49:315–331. https://doi.org/10.1016/j.jvcir.2017.09.009

    Article  Google Scholar 

  2. Bay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. In: Computer vision–ECCV 2006. Springer, pp 404–417

  3. Babenko A, Slesarev A, Chigorin A, Lempitsky VS (2014) Neural codes for image retrieval. In: Computer Vision - ECCV 2014 - 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I, pp 584–599, https://doi.org/10.1007/978-3-319-10590-1_38. https://dblp.org/rec/bib/conf/eccv/BabenkoSCL14. dblp computer science bibliography, https://dblp.org

  4. Bosch A, Zisserman A, Munoz X (2007) Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, CIVR 2007, Amsterdam, The Netherlands, July 9-11, 2007, pp 401–408, https://doi.org/10.1145/1282280.1282340. https://dblp.org/rec/bib/conf/civr/BoschZM07. dblp computer science bibliography, https://dblp.org

  5. Chatzichristofis S, Boutalis Y (2008) Cedd: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. Comput Vis Syst 5008:312–322

    Article  Google Scholar 

  6. Chatzichristofis SA, Zagoris K, Boutalis YS, Papamarkos N (2010) Accurate image retrieval based on compact composite descriptors and relevance feedback information. Int J Pattern Recogn Artif Intell (IJPRAI) 2:207–244

    Article  Google Scholar 

  7. Chatzichristofis SA, Iakovidou C, Boutalis YS, Oge M (2013) Co.vi.wo.: color visual words based on non-predefined size codebooks. IEEE Trans Cybern 43(1):192–205

    Article  Google Scholar 

  8. Csurka G, Dance C, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, ECCV, vol 1

  9. Deselaers T, Keysers D, Ney H (2008) Features for image retrieval: an experimental comparison. Inf Retr 11(2):77–107

    Article  Google Scholar 

  10. Eisa M, Eletrebi A, Elhenawy E (2013) Enhancing the retrieval performance by combing the texture and edge features. CoRR, arXiv:abs/1301.2542

  11. Fond A, Berger M-O, Simon G (2017) Facade proposals for urban augmented reality. In: 16th IEEE International symposium on mixed and augmented reality (ISMAR)

  12. Gholipour F, Ebrahimnezhad H (2014) An efficient content based image retrieval using edge orientation co-occurrence matrix. In: 2014 6th Conference on information and knowledge technology (IKT). IEEE, pp 67–72

  13. Gordo A, Almazȧn J, Revaud J, Larlus D (2016) Deep image retrieval: learning global representations for image search. In: Computer Vision - ECCV 2016 - 14th European conference. Amsterdam, The Netherlands, October 11-14, 2016, Proceedings Part VI, pp 241–257

  14. Harris CG, Pike JM (1988) 3d positional integration from image sequences. Image Vis Comput 6(2):87–90

    Article  Google Scholar 

  15. Huang J, Kumar SR, Mitra M, Zhu WJ (2001) Image indexing using color correlograms. US Patent 6,246,790 12:1–16

  16. Iakovidou C, Anagnostopoulos N, Kapoutsis A, Boutalis Y, Lux M, Chatzichristofis SA (2015) Localizing global descriptors for content-based image retrieval. EURASIP J Adv Signal Process 2015(1):80

    Article  Google Scholar 

  17. Jain M, Jégou H, Gros P (2011) Asymmetric hamming embedding: taking the best of our bits for large scale image search. In: ACM Multimedia. ACM Multimedia, Nov 28 - Dec 1, Scottsdale, Arizona, USA. ACM, Scottsdale, pp 1441–1444

  18. Jegou H, Douze M, Schmid C (2008) Hamming embedding and weak geometric consistency for large scale image search. In: Computer Vision - ECCV 2008, 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part I, pp 304–317, https://doi.org/10.1007/978-3-540-88682-2_24. https://dblp.org/rec/bib/conf/eccv/JegouDS08, dblp computer science bibliography, https://dblp.org

  19. Jégou H, Douze M, Schmid C (2010) Improving bag-of-features for large scale image search. Int J Comput Vis 87(3):316–336

    Article  Google Scholar 

  20. Jėgou H, Perronnin F, Douze M, Sȧnchez J, Pėrez P, Schmid C (2012) Aggregating local image descriptors into compact codes. IEEE Trans Pattern Anal Mach Intell 34(9):1704–1716

    Article  Google Scholar 

  21. Jianxin Wu, Rehg JM (2011) Centrist: a visual descriptor for scene categorization. IEEE Trans Pattern Anal Mach Intell 33(8):1489–1501

    Article  Google Scholar 

  22. Karakasis EG, Amanatiadis A, Gasteratos A, Chatzichristofis SA (2015) Image moment invariants as local features for content based image retrieval using the bag-of-visual-words model. Pattern Recogn Lett 55(0):22–27

    Article  Google Scholar 

  23. Kim NW, Kim TY, Choi J-S (2005) Edge-based spatial descriptor for content-based image retrieval. In: Image and Video Retrieval, 4th International Conference, CIVR 2005, Singapore, July 20-22, 2005, Proceedings, pp 454–464, https://doi.org/10.1007/11526346_49. https://dblp.org/rec/bib/conf/civr/KimKC05. dblp computer science bibliography, https://dblp.org

  24. Le D, Liang Y, Kong G, Zhang Q, Cao X, Izquierdo E (2016) Holons visual representation for image retrieval. IEEE Trans Multimed 18(4):714–725

    Article  Google Scholar 

  25. Lei Z, Fuzong L, Bo Z (1999) A cbir method based on color-spatial feature. In: TENCON 99. Proceedings of the IEEE Region 10 conference, vol 1. IEEE, Cheju Island, South Korea, South Korea. pp 166–169

  26. Leutenegger S, Chli M, Siegwart RY (2011) Brisk: binary robust invariant scalable keypoints. In 2011 IEEE International conference on computer vision (ICCV). IEEE, Barcelona, Spain. pp 2548– 2555

  27. Li X, Larson M, Hanjalic A (2015) Pairwise geometric matching for large-scale object retrieval. In: IEEE Conference on computer vision and pattern recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015, pp 5153–5161

  28. Li C, Huang Y, Zhu L (2017) Color texture image retrieval based on gaussian copula models of gabor wavelets. Pattern Recogn 64:118–129

    Article  Google Scholar 

  29. Lowe DG (1999) Object recognition from local scale-invariant features. In: The proceedings of the seventh IEEE international conference on computer vision, 1999, vol 2. IEEE, Kerkyra, Greece, Greece. pp 1150–1157

  30. Lux M, Chatzichristofis SA (2008) Lire: lucene image retrieval: an extensible java CBIR library. In: Proceedings of the 16th International Conference on Multimedia 2008, Vancouver, British Columbia, Canada, October 26-31, 2008, pp 1085–1088, https://doi.org/10.1145/1459359.1459577. https://dblp.org/rec/bib/conf/mm/LuxC08. dblp computer science bibliography, https://dblp.org

  31. Lux M, Anagnostopoulos N, Iakovidou C (2016) Spatial pyramids for boosting global features in content based image retrieval. In: 14th International Workshop on Content-Based Multimedia Indexing, CBMI 2016, Bucharest, Romania, June 15-17, 2016. IEEE, pp 1–4, https://doi.org/10.1109/CBMI.2016.7500248. https://dblp.org/rec/bib/conf/cbmi/LuxAI16. dblp computer science bibliography, https://dblp.org

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

    Article  Google Scholar 

  33. Mei T, Rui Y, Li S, Qi T (2014) Multimedia search reranking: a literature survey. ACM Comput Surv (CSUR) 46(3):38

    Article  Google Scholar 

  34. Mikulík A, Perdoch M, Chum O, Matas J (2010) Learning a fine vocabulary. In: Computer vision - ECCV 2010, 11th European conference on computer vision, Heraklion, Crete, Greece, September 5-11, 2010. Proceedings, Part III, pp 1–14

  35. Ng JY-H, Yang F, Davis LS (2015) Exploiting local features from deep networks for image retrieval. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 53–61

  36. Nister D, Stewenius H (2006) Scalable recognition with a vocabulary tree. In: Proc. CVPR, vol 5. Citeseer

  37. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  38. Papakostas GA, Koulouriotis DE, Karakasis EG (2009) A unified methodology for the efficient computation of discrete orthogonal image moments. Inf Sci 179(20):3619–3633

    Article  MathSciNet  Google Scholar 

  39. Papakostas GA, Koulouriotis DE, Karakasis E, Tourassis VD (2013) Moment-based local binary patterns: a novel descriptor for invariant pattern recognition applications. Neurocomputing 99:358– 371

    Article  Google Scholar 

  40. Paulin M, Douze M, Harchaoui Z, Mairal J, Perronin F, Schmid C (2015) Local convolutional features with unsupervised training for image retrieval. In: Proceedings of the IEEE international conference on computer vision, pp 91–99

  41. Perronnin F, Liu Y, Sȧnchez J, Poirier H (2010) Large-scale image retrieval with compressed fisher vectors. In: The Twenty-Third IEEE conference on computer vision and pattern recognition, CVPR 2010, San Francisco, CA, USA, 13-18 June 2010, pp 3384–3391

  42. Perronnin F, Sánchez J, Mensink T (2010) Improving the fisher kernel for large-scale image classification. In: Computer Vision - ECCV 2010, 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part IV, pp 143–156, https://doi.org/10.1007/978-3-642-15561-1_11. https://dblp.org/rec/bib/conf/eccv/PerronninSM10, dblp computer science bibliography, https://dblp.org

  43. Petscharnig S, Lux M, Chatzichristofis S (2017) Dimensionality reduction for image features using deep learning and autoencoders. In: Proceedings of the 15th international workshop on content-based multimedia indexing CBMI ’17, pp 23:1–23:6

  44. Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2008) Lost in quantization: improving particular object retrieval in large scale image databases. In: IEEE Conference on computer vision and pattern recognition, 2008. CVPR 2008. IEEE, pp 1–8

  45. Qi G-J, Hua X-S, Rui Y, Mei T, Tang J, Zhang H-J (2007) Concurrent multiple instance learning for image categorization. In: 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 18-23 June 2007, Minneapolis, Minnesota, USA, https://doi.org/10.1109/CVPR.2007.383152. https://dblp.org/rec/bib/conf/cvpr/QiHRMTZ07. dblp computer science bibliography, https://dblp.org

  46. Razavian AS, Azizpour H, Sullivan J, Stefan C (2014) CNN features off-the-shelf: an astounding baseline for recognition. In: IEEE Conference on computer vision and pattern recognition, CVPR Workshops 2014, Columbus, OH, USA, June 23-28, 2014, pp 512–519

  47. Reta C, Solis-Moreno I, Cantoral-Ceballos JA, Alvarez-Vargas R, Townend P (2017) Improving content-based image retrieval for heterogeneous datasets using histogram-based descriptors. Multimedia Tools and Applications, https://doi.org/10.1007/s11042-017-4708-8. ISSN: 1573-7721

  48. Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In: ECCV (1), pp 430–443

  49. Rublee E, Rabaud V, Konolige K, Bradski GR (2011) Orb: an efficient alternative to sift or surf. In: ICCV, 6-13 November, Barcelona, Spain. IEEE, Barcelona, pp 2564–2571

  50. Sajjad M, Ullah A, Ahmad J, Abbas N, Rho S, Baik SW (2018) Integrating salient colors with rotational invariant texture features for image representation in retrieval systems. Multimed Tools Appl 77(4):4769–4789. https://doi.org/10.1007/s11042-017-5010-5. https://dblp.org/rec/bib/journals/mta/SajjadUAARB18, dblp computer science bibliography, https://dblp.org

    Article  Google Scholar 

  51. Schaefer G, Stich M (2004) Ucid: an uncompressed color image database. Storage Retriev Methods Appl Multimed 5307:472–480

    Google Scholar 

  52. Shao H, Svoboda T, Van Gool L (2003) Zubud-zurich buildings database for image based recognition. Computer Vision Lab, Swiss Federal Institute of Technology, Switzerland, Tech. Rep. 260:20

  53. Shi J, Tomasi C (1994) Good features to track. In: Conference on Computer Vision and Pattern Recognition, CVPR 1994, 21-23 June, 1994, Seattle, WA, USA, pp 593–600, https://doi.org/10.1109/CVPR.1994.323794. https://dblp.org/rec/bib/conf/cvpr/ShiT94. dblp computer science bibliography, https://dblp.org

  54. Shyu C-R, Brodley CE, Kak AC, Kosaka A, Aisen A, Broderick L (1998) Local versus global features for content-based image retrieval. In: IEEE Workshop on content-based access of image and video libraries proceedings. IEEE, Santa Barbara, USA. pp 30–34

  55. Tolias G, Hervė J (2014) Visual query expansion with or without geometry: refining local descriptors by feature aggregation. Pattern Recogn 47(10):3466–3476

    Article  Google Scholar 

  56. Tolias G, Avrithis YS, Jėgou H (2013) To aggregate or not to aggregate: selective match kernels for image search. In: IEEE International conference on computer vision, , ICCV 2013, Sydney, Australia, December 1-8, 2013 pp 1401–1408

  57. Tolias G, Avrithis YS, Hervė J (2016) Image search with selective match kernels: aggregation across single and multiple images. Int J Comput Vis 116(3):247–261

    Article  MathSciNet  Google Scholar 

  58. Vassou SA, Anagnostopoulos N, Amanatiadis A, Christodoulou K, Chatzichristofis SA (2017) Como: a compact composite moment-based descriptor for image retrieval. In: Proceedings of the 15th international workshop on content-based multimedia indexing, CBMI 2017, Florence, Italy, June 19-21, 2017. pp. 30:1–30:5

  59. Wang X, Yang M, Cour T, Zhu S, Yu K, Han TX (2011) Contextual weighting for vocabulary tree based image retrieval. In: IEEE International conference on computer vision, ICCV 2011, Barcelona, Spain, November 6-13, 2011. pp 209–216

  60. Won CS, Park DK, Park SJ (2002) Efficient use of mpeg-7 edge histogram descriptor. Etri J 24(1):23–30

    Article  Google Scholar 

  61. Zhang S, Yang M, Wang X, Lin Y, Qi T (2013) Semantic-aware co-indexing for image retrieval. In: Proceedings of the IEEE international conference on computer vision, pp 1673– 1680

  62. Zhang S, Yang M, Wang X, Lin Y, Tian Q (2015) Semantic-aware co-indexing for image retrieval. IEEE Trans Pattern Anal Mach Intell 37(12):2573–2587

    Article  Google Scholar 

  63. Zheng L, Wang S, Qi T (2014) Coupled binary embedding for large-scale image retrieval. IEEE Trans Image Process 23(8):3368–3380

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cyprus University of Technology, Limassol, Cyprus

    S. A. Vassou

  2. Microsoft, Prague, Czech Republic

    N. Anagnostopoulos

  3. Department of Information Sciences, Neapolis University Pafos, Pafos, Cyprus

    K. Christodoulou & S. A. Chatzichristofis

  4. Democritus University of Thrace, Xanthi, Greece

    A. Amanatiadis

Authors
  1. S. A. Vassou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Anagnostopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Christodoulou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Amanatiadis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S. A. Chatzichristofis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. A. Chatzichristofis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vassou, S.A., Anagnostopoulos, N., Christodoulou, K. et al. CoMo: a scale and rotation invariant compact composite moment-based descriptor for image retrieval. Multimed Tools Appl 78, 2765–2788 (2019). https://doi.org/10.1007/s11042-018-5854-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-5854-3

Keywords

Search

Navigation