Multimedia Tools and Applications

, Volume 77, Issue 7, pp 8163–8193 | Cite as

Improving content-based image retrieval for heterogeneous datasets using histogram-based descriptors

  • Carolina Reta
  • Ismael Solis-Moreno
  • Jose A. Cantoral-Ceballos
  • Rogelio Alvarez-Vargas
  • Paul Townend


Image content analysis plays a key role in areas such as image classification, clustering, indexing, retrieving, and object and scene recognition. However, although several image content descriptors have been proposed in the literature, their low performance score or high computational cost makes them unsuitable for content-based image retrieval on large datasets. This paper presents an efficient content-based image retrieval approach that uses histogram-based descriptors to represent color, edge, and texture features, and a k-nearest neighbor classifier to retrieve the best matches for query images. The compactness and speed of the proposed descriptors allow their application in heterogeneous photographic collections whilst showing strong image discrimination in the presence of significant content variation. Experimentation was conducted on four different image collections using four distance metrics. The results show that the proposed approach consistently achieves noteworthy mean average precision, recall, and precision measures. It outperforms state-of-the-art approaches based on the MPEG 7 descriptors (SCD, CLD, and EHD), whilst producing comparable results to those achieved by novel SIFT-based and SURF-based approaches that require more complex data manipulation.


Image retrieval Visual features Lab color descriptor Gabor wavelets Local binary patterns Histograms 


  1. 1.
    Agrawal R, Grosky WI, Fotouhi F (2010) Image clustering and retrieval using MPEG-7. The handbook of MPEG applications. Wiley, Chichester, UK, pp 221–239CrossRefGoogle Scholar
  2. 2.
    Beaubouef T, Petry FE, Ladner R (2007) Spatial data methods and vague regions: a rough set approach. Appl Soft Comput. doi: 10.1016/j.asoc.2004.11.003
  3. 3.
    Bober M (2001) MPEG-7 Visual shape descriptors. IEEE T Circ Syst Vid. doi: 10.1109/76.927426
  4. 4.
    Castelli V, Bergman LD (2002) Image databases: search and retrieval of digital imagery. Wiley, New YorkGoogle Scholar
  5. 5.
    Chatzichristofis SA, Iakovidou C, Boutalis YS, Angelopoulou E (2014) Mean normalized retrieval order (MNRO): a new content-based image retrieval performance measure. Multimed Tools Appl. doi: 10.1007/s11042-012-1192-z
  6. 6.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. IEEE I Conf Comput Vision Pattern Recogn. doi: 10.1109/CVPR.2005.177
  7. 7.
    Deselaers T, Keysers D, Ney H (2008) Features for image retrieval: an experimental comparison. Inform Retrieval. doi: 10.1007/s10791-007-9039-3
  8. 8.
    Feng D, Siu WC, Zhang HJ (2003) Multimedia information retrieval and management: technological fundamentals and applications. Springer, Berlin. doi: 10.1007/978-3-662-05300-3 CrossRefzbMATHGoogle Scholar
  9. 9.
    Francos JM, Meiri A, Porat B (1993) A unified texture model based on a 2-d wold-like decomposition. IEEE T Signal Proces. doi: 10.1109/78.229897
  10. 10.
    Gevers T, Stokman H (2004) Robust histogram construction from color invariants for object recognition. IEEE T Pattern Anal. doi: 10.1109/TPAMI.2004.1261083
  11. 11.
    Huang J, Kumar SR, Mitra M, Zhu WJ, Zabih R (1997) Image indexing using color correlograms. IEEE I Conf Comput Vision Pattern Recogn. doi: 10.1109/CVPR.1997.609412
  12. 12.
    Iakovidou C, Anagnostopoulos N, Kapoutsis A, Boutalis Y, Lux M, Chatzichristofis SA (2015) Localizing global descriptors for content-based image retrieval. EURASIP J Adv Sig Pr. doi: 10.1186/s13634-015-0262-6
  13. 13.
    Jain M, Jégou H, Gros P (2011) Asymmetric hamming embedding: taking the best of our bits for large scale image search. ACM I Conf Multimed. doi: 10.1145/2072298.2072035
  14. 14.
    Jégou H, Douze M, Schmid C (2009) Improving bag-of-features for large scale image search. Int J Comput Vision. doi: 10.1007/s11263-009-0285-2
  15. 15.
    Jégou H, Matthijs D, Schmid C (2008) Hamming embedding and weak geometry consistency for large scale image search. Eur Conf Comp Vis. doi: 10.1007/978-3-540-88682-2_24
  16. 16.
    Juneja K, Verma A, Goel S, Goel S (2015) A survey on recent image indexing and retrieval techniques for low-level feature extraction in CBIR systems. IEEE I Conf Comput Intell Commun Techn. doi: 10.1109/CICT.2015.92
  17. 17.
    Kailath T (1967) The divergence and bhattacharyya distance measures in signal selection. IEEE T Communi Techn. doi: 10.1109/TCOM.1967.1089532
  18. 18.
    Kumar G, Bhatia PK (2014) A detailed review of feature extraction in image processing systems. IEEE I Conf Adv Comp Commun Techn. doi: 10.1109/ACCT.2014.74
  19. 19.
    Manjunath BS, Ohm JR, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE T Circ Syst Vid. doi: 10.1109/76.927424
  20. 20.
    MATLAB R2015b+ (2015) The Mathworks Inc, MassachusettsGoogle Scholar
  21. 21.
    Nie L, Wang M, Zha Z, Chua TS (2012) Oracle in image search: a content-based approach to performance prediction. ACM T Inform Syst. doi: 10.1145/2180868.2180875
  22. 22.
    Nie L, Wang M, Zha Z, Li G, Chua TS (2011) Multimedia answering: enriching text QA with media information. ACM I Conf Res Dev Inform Retrieval. doi: 10.1145/2009916.2010010
  23. 23.
    Nie L, Yan S, Wang M, Hong R, Chua TS (2012) Harvesting visual concepts for image search with complex queries. ACM I Conf Multimed. doi: 10.1145/2393347.2393363
  24. 24.
    Nister D, Stewenius H (2006) Scalable recognition with a vocabulary tree. IEEE I Conf Comput Vision Pattern Recogn. doi: 10.1109/CVPR.2006.264
  25. 25.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE T Pattern Anal. doi: 10.1109/TPAMI.2002.1017623
  26. 26.
    Paschos G (2001) Perceptually uniform color spaces for color texture analysis: an empirical evaluation. IEEE T Image Process. doi: 10.1109/83.923289
  27. 27.
    Pass G, Zabih R, Miller J (1996) Comparing images using color coherence vectors. ACM I Conf Multimed. doi: 10.1145/244130.244148
  28. 28.
    Rao AR, Lohse GL (1996) Towards a texture naming system: identifying relevant dimensions of texture. Vision Res. doi: 10.1016/0042-6989(95)00202-2
  29. 29.
    Roy AJ, Stell JG (2001) Spatial relations between indeterminate regions. Int J Approx Reason. doi: 10.1016/S0888-613X(01)00033-0
  30. 30.
    Rubner Y, Tomasi C, Guibas J (2000) The earth mover’s distance as a metric for image retrieval. Int J Comput Vision. doi: 10.1023/A:1026543900054
  31. 31.
    Schaefer G, Stich M (2003) UCID: An uncompressed color image database. P Soc Photo-Opt Ins. doi: 10.1117/12.525375
  32. 32.
    Shao H, Svoboda T, Tuytelaars T, Van Gool L (2003) HPAT Indexing for fast object/scene recognition based on local appearance. Lect Notes Comput Sc. doi: 10.1007/3-540-45113-7_8
  33. 33.
    Shereena VB, David JM (2014) Content based image retrieval: classification using neural networks. Int J Multimed Appl. doi: 10.5121/ijma.2014.6503
  34. 34.
    Sikora T (2001) The MPEG-7 visual standard for content description-an overview. IEEE T Circ Syst Vid. doi: 10.1109/76.927422
  35. 35.
    Tamura H, Mori S, Yamawaki T (1978) Textural features corresponding to visual perception. IEEE Sys Man Cybern. doi: 10.1109/TSMC.1978.4309999
  36. 36.
    Vegad SP, Italiya PK (2015) Image classification using neural network for efficient image retrieval. IEEE I Conf Electr Electron Signals Commun Optimizat. doi: 10.1109/EESCO.2015.7253860
  37. 37.
    Wong KM, Po LM, Cheung KW (2007) A compact and efficient color descriptor for image retrieval. IEEE I Conf Multimed Expo. doi: 10.1109/ICME.2007.4284724
  38. 38.
    Wu YN, Si Z, Gong H, Zhu SC (2009) Learning active basis model for object detection and recognition. Int J Comput Vision. doi: 10.1007/s11263-009-0287-0
  39. 39.
    Yan C, Zhang Y, Dai F, Li L (2013) Highly parallel framework for HEVC motion estimation on many-core platform. IEEE Data Compr Conf. doi: 10.1109/DCC.2013.14
  40. 40.
    Yan C, Zhang Y, Dai F, Zhang J, Li L, Dai Q (2014) Efficient parallel HEVC intra-prediction on many-core processor. Electron Lett. doi: 10.1049/el.2014.0611
  41. 41.
    Yan C, Zhang Y, Xu J, Dai F, Zhang J, Dai Q, Wu F (2014) Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE T Circ Syst Vid. doi: 10.1109/TCSVT.2014.2335852
  42. 42.
    Zhang S, Yang M, Wang X, Lin Y, Tian Q (2013) Semantic-aware co-indexing for image retrieval. IEEE I Conf Comp Vis. doi: 10.1109/ICCV.2013.210
  43. 43.
    Zheng L, Wang S, Tian Q (2014) Coupled binary embedding for large-scale image retrieval. IEEE T Image Process. doi: 10.1109/TIP.2014.2330763

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Division of IT, Electronic & ControlCONACYT-CIATEQHidalgoMexico
  2. 2.Mexico Software Lab.IBMJaliscoMexico
  3. 3.Division of IT, Electronic & ControlCIATEQQueretaroMexico
  4. 4.School of ComputingUniversity of LeedsLeedsUK

Personalised recommendations