Partition selection with sparse autoencoders for content based image classification

Abstract

Managing colossal image datasets with large dimensional hand-crafted features is no more feasible in most of the cases. Content based image classification (CBIC) of these large image datasets calls for the need of dimensionality reduction of features extracted for the purpose. This paper identifies the escalating challenges in the discussed domain and introduces a technique of feature dimension reduction by means of identifying region of interest in a given image with the use of reconstruction errors computed by sparse autoencoders. The automated process identifies the significant regions in an image for feature extraction. It not only improves the dimension of useful features but also contributes to increased classification results compared to earlier approaches. The reduction in number of one kind of features easily makes space for the inclusion of other features whose fusion facilitates improved classification performance compared to individual feature extraction techniques. Two different datasets, i.e. Wang dataset and Corel 5K dataset have been used for the experiments. State-of-the-art classifiers, i.e. Support Vector Machine and Extreme Learning Machine are used for CBIC. The proposed techniques are evaluated and compared in the context of both the classifiers and analysis of results suggests the appropriateness of the proposed methods for real time applications.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 1.

    Miao RH, Tang JL, Chen XQ (2015) Classification of farmland images based on color features. J Vis Commun Image Represent 29:138–146

    Article  Google Scholar 

  2. 2.

    Zand M, Doraisamy S, Halin AA, Mustaffa MR (2015) Texture classification and discrimination for region-based image retrieval. J Vis Commun Image Represent 26:305–316

    Article  Google Scholar 

  3. 3.

    Ping Tian D (2013) A review on image feature extraction and representation techniques. Int J Multimed Ubiquitous Eng 8(4):385–396

    Google Scholar 

  4. 4.

    Lin Y, Lv F, Zhu S, Yang M, Cour T, Yu K, Huang T (2011) Large-scale image classification: fast feature extraction and SVM training. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1689–1696

  5. 5.

    Ekenel HK, Stiefelhagen R (2006) Block selection in the local appearance-based face recognition scheme. In: Conference on computer vision and pattern recognition workshop, 2006. CVPRW’06. IEEE, pp 43–43

  6. 6.

    Camlica Z, Tizhoosh HR, Khalvati F (2015) Autoencoding the retrieval relevance of medical images. In: 2015 International conference on image processing theory, tools and applications (IPTA). IEEE, pp 550–555

  7. 7.

    Topi M, Timo O, Matti P, Maricor S (2000) Robust texture classification by subsets of local binary patterns. In: Proceedings of the 15th international conference on pattern recognition, 2000, vol 3. IEEE, pp 935–938

  8. 8.

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

    Article  Google Scholar 

  9. 9.

    Walia E, Pal A (2014) Fusion framework for effective color image retrieval. J Vis Commun Image Represent 25(6):1335–1348

    Article  Google Scholar 

  10. 10.

    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1. IEEE, pp 886–893

  11. 11.

    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, pp 1150–1157

  12. 12.

    Dunham MH (2006) Data mining: introductory and advanced topics. Pearson Education India, Bengaluru

    Google Scholar 

  13. 13.

    Kotsiantis SB, Zaharakis I, Pintelas P (2007) Supervised machine learning: a review of classification techniques. Informatica 31:249–268

    MathSciNet  Google Scholar 

  14. 14.

    Weise T, Chiong R (2010) Evolutionary data mining approaches for rule-based and tree-based classifiers. In: 2010 9th IEEE international conference on cognitive informatics (ICCI). IEEE, pp 696–703

  15. 15.

    Dasgupta S, Kalai AT, Monteleoni C (2009) Analysis of perceptron-based active learning. J Mach Learn Res 10(Feb):281–299

    MathSciNet  MATH  Google Scholar 

  16. 16.

    Huang G, Huang GB, Song S, You K (2015) Trends in extreme learning machines: a review. Neural Netw 61:32–48

    Article  MATH  Google Scholar 

  17. 17.

    Huang X, Shi L, Suykens JA (2014) Support vector machine classifier with pinball loss. IEEE Trans Pattern Anal Mach Intell 36(5):984–997

    Article  Google Scholar 

  18. 18.

    Zhou XS, Huang TS (2000) CBIR: from low-level features to high-level semantics. In: Proceedings of SPIE—the international society for optical engineering, vol 3974, pp 426–431

  19. 19.

    Talib A, Mahmuddin M, Husni H, George LE (2013) A weighted dominant color descriptor for content-based image retrieval. J Vis Commun Image Represent 24(3):345–360

    Article  Google Scholar 

  20. 20.

    Brunelli R, Mich O (2001) Histograms analysis for image retrieval. Pattern Recogn 34(8):1625–1637

    Article  MATH  Google Scholar 

  21. 21.

    Rasheed W, An Y, Pan S, Jeong I, Park J, Kang J (2008) Image retrieval using maximum frequency of local histogram based color correlogram. In: International conference on multimedia and ubiquitous engineering, 2008. MUE 2008. IEEE, pp 62–66

  22. 22.

    Huang J, Kumar SR, Mitra M, Zhu WJ, Zabih R (1997) Image indexing using color correlograms. In: Proceedings of the 1997 IEEE computer society conference on computer vision and pattern recognition, 1997. IEEE, pp 762–768

  23. 23.

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

    Article  Google Scholar 

  24. 24.

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

    Article  Google Scholar 

  25. 25.

    Park DK, Jeon YS, Won CS (2000) Efficient use of local edge histogram descriptor. In: Proceedings of the 2000 ACM workshops on multimedia. ACM, pp 51–54

  26. 26.

    Yuan LUO, Wu CM, Zhang Y (2013) Facial expression feature extraction using hybrid PCA and LBP. J China Univ Posts Telecommun 20(2):120–124

    Article  Google Scholar 

  27. 27.

    Wang W, Huang Y, Wang Y, Wang L (2014) Generalized autoencoder: a neural network framework for dimensionality reduction. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 490–497

  28. 28.

    Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  29. 29.

    Zhu Q, Shyu ML (2015) Sparse linear integration of content and context modalities for semantic concept retrieval. IEEE Trans Emerg Top Comput 3(2):152–160

    Article  Google Scholar 

  30. 30.

    ElAlami ME (2011) A novel image retrieval model based on the most relevant features. Knowl Based Syst 24(1):23–32

    Article  Google Scholar 

  31. 31.

    Hiremath PS, Pujari J (2007) Content based image retrieval using color, texture and shape features. In: International conference on advanced computing and communications, 2007. ADCOM 2007. IEEE, pp 780–784

  32. 32.

    Yue J, Li Z, Liu L, Fu Z (2011) Content-based image retrieval using color and texture fused features. Math Comput Model 54(3):1121–1127

    Article  Google Scholar 

  33. 33.

    Banerjee M, Kundu MK, Maji P (2009) Content-based image retrieval using visually significant point features. Fuzzy Sets Syst 160(23):3323–3341

    MathSciNet  Article  Google Scholar 

  34. 34.

    Jalab HA (2011) Image retrieval system based on color layout descriptor and Gabor filters. In: 2011 IEEE conference on open systems (ICOS). IEEE, pp 32–36

  35. 35.

    Shen GL, Wu XJ (2013) Content based image retrieval by combining color, texture and CENTRIST. IEEE Int Workshop Signal Process 1:1–4

    Google Scholar 

  36. 36.

    Irtaza A, Jaffar MA, Aleisa E, Choi TS (2014) Embedding neural networks for semantic association in content based image retrieval. Multimed Tools Appl 72(2):1911–1931

    Article  Google Scholar 

  37. 37.

    Rahimi M, Moghaddam ME (2015) A content-based image retrieval system based on color ton distribution descriptors. SIViP 9(3):691–704

    Article  Google Scholar 

  38. 38.

    ElAlami ME (2014) A new matching strategy for content based image retrieval system. Appl Soft Comput 14:407–418

    Article  Google Scholar 

  39. 39.

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

    Article  Google Scholar 

  40. 40.

    Walia E, Vesal S, Pal A (2014) An effective and fast hybrid framework for color image retrieval. Sens Imaging 15(1):93

    Article  Google Scholar 

  41. 41.

    Subrahmanyam M, Maheshwari RP, Balasubramanian R (2012) Expert system design using wavelet and color vocabulary trees for image retrieval. Expert Syst Appl 39(5):5104–5114

    Article  Google Scholar 

  42. 42.

    Wang Y, Yao H, Zhao S, Zheng Y (2015) Dimensionality reduction strategy based on auto-encoder. In: Proceedings of the 7th international conference on internet multimedia computing and service. ACM, p 63

  43. 43.

    Liu GH, Yang JY (2013) Content-based image retrieval using color difference histogram. Pattern Recogn 46(1):188–198

    Article  Google Scholar 

Download references

Acknowledgements

We appreciate the suggestions and comments of the anonymous reviewers which have helped us raise the standard of the paper significantly.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Rik Das.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Das, R., Walia, E. Partition selection with sparse autoencoders for content based image classification. Neural Comput & Applic 31, 675–690 (2019). https://doi.org/10.1007/s00521-017-3099-0

Download citation

Keywords

  • Content based image classification (CBIC)
  • Dimension reduction
  • Early fusion
  • Autoencoders
  • Extreme Learning Machine (ELM)
  • Support Vector Machine (SVM)
  • Feature extraction
  • Partition selection