Skip to main content
SpringerLink
Log in

Categorization of Images Using Autoencoder Hashing and Training of Intra Bin Classifiers for Image Classification and Annotation

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Automatic annotation of images is considered to be an important research problem in image retrieval. Traditional methods are computationally complex and fail to annotate correctly when the number of image classes is large and related. This paper proposes a novel approach, an autoencoder hashing, to categorize images of large-scale image classes. The intra bin classifiers are trained to classify the query image, and the tag weight and tag frequency are computed to achieve a more effective annotation of the query image. The proposed approach has been compared with other existing approaches in the literature using performance measures, such as precision, accuracy, mean average precision (MAP), and F1 score. The experimental results indicate that our proposed approach outperforms the existing approaches.

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

References

  1. Liu, P., Choo, K.-K. R., Wang, L., and Huang, F., SVM or deep learning? A comparative study on remote sensing image classification. Soft Computing 21(23):7053–7065, 2017.

    Article  Google Scholar 

  2. Wang, L., Zhang, J., Liu, P., Choo, K.-K. R., and Huang, F., Spectral-spatial multi-feature based deep learning for hyperspectral remote sensing image classification. Soft Computing 21(1):213–221, 2017.

    Article  Google Scholar 

  3. D’Orazio, C., and Choo, K.-K. R., An adversary model to evaluate DRM protection of video contents on iOS devices. Computers & Security 56:94–110, 2016.

    Article  Google Scholar 

  4. Anbarasan, K., and Chitrakala, S., Clustering-based color image segmentation using local maxima. International Journal of Intelligent Information Technologies 14(1):28–47, 2018.

    Article  Google Scholar 

  5. Bouramoul, A., Gravizor: A graphical tool for the visualization of web search engines results with multi-agent modeling. International Journal of Intelligent Information Technologies 13(3):37–56, 2017.

    Article  Google Scholar 

  6. Raghuveera, T., Vidhushini, S., and Swathi, M., Comparative study of CAMSHIFT and RANSAC methods for face and eye tracking in real-time video. International Journal of Intelligent Information Technologies 13(2):63–75, 2017.

    Article  Google Scholar 

  7. Chen, H. Y., Choo, K.-K. R., and Chen, W. U., Tamper detection and image recovery for BTC-compressed images. Multimedia Tools and Applications 76(14):15435–15463, 2017.

    Article  Google Scholar 

  8. Mohammadi, R., and Javidan, R., An intelligent traffic engineering method over software defined networks for video surveillance systems based on artificial bee colony. International Journal of Intelligent Information Technologies 12(4):45–62, 2016.

    Article  Google Scholar 

  9. Bagiwa, M. A., Wahab, A. W. A., Idris, M. Y. I., Khan, S., and Choo, K.-K. R., Chroma key background detection for digital video using statistical correlation of blurring artifact. Digital Investigation 19:29–43, 2016.

    Article  Google Scholar 

  10. Priyatharshini, R., and Chitrakala, S., An efficient coronary disease diagnosis system using dual-phase multi-objective and embedded feature selection. International Journal of Intelligent Information Technologies 13(3):15–36, 2017.

    Article  Google Scholar 

  11. Li, J., and Wang, J. Z., Automatic linguistic indexing of pictures by a statistical modelling approach. IEEE Tr. On Pattern Analysis & Machine Intelligence 25(9):1075–1088, 2003. https://doi.org/10.1109/TPAMI.2003.1227984.

    Article  Google Scholar 

  12. Luo, J., Savakis, A. E., and Singhal, A., A Bayesian network-based framework for semantic image understanding. Pattern Recognition 38(6):919–934, 2005. https://doi.org/10.1016/j.patcog.2004.11.00.

    Article  Google Scholar 

  13. Christos, D., George, S., Panagiotis, P., Christos, P., Nikos, D., and Anastasios, D., Large scale concept detection in multimedia data using small training sets and cross domain concept fusion. IEEE Tr. on Circuits and Systems for Video Technology 20(12):1808–1822, 2010. https://doi.org/10.1109/TCSVT.2010.2087814.

    Article  Google Scholar 

  14. Cabral, R., De la Torre, F., Costeira, J. P., and Bernardino, A., Matrix completion for weakly-supervised multi-label image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 37(1):121–135, 2015. https://doi.org/10.1109/TPAMI.2014.2343234.

    Article  PubMed  Google Scholar 

  15. Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., and Herrera, F., An overview of ensemble methods for binary classifiers in multi-class problems: An experimental study on one-vs-one and one-vs-all schemes. Pattern Recognition, Elsevier 44(8):1761–1776, 2011. https://doi.org/10.1016/j.patcog.2011.01.017.

    Article  Google Scholar 

  16. Hsu, C. W., and Lin, C. J., A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13(2):415–425, 2002.

    Article  PubMed  Google Scholar 

  17. Polat, K., and Gune, S., A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Systems with Applications 36(2):1587–1592, 2009.

    Article  Google Scholar 

  18. Yu, X., Aloimonos, Y., Attribute-based transfer learning for object categorization with zero/one training example. European Conference on Computer Vision pp 127–140. doi:https://doi.org/10.1007/978-3-642-15555-0_10, 2010.

  19. Miao, Q., Liu, R., Zhao, P., Li, Y., and Sun, E., A semi-supervised image classification model based on improved ensemble projection algorithm. IEEE Access 6:1372–1379, 2017. https://doi.org/10.1109/ACCESS.2017.2778881.

    Article  Google Scholar 

  20. Miller, G., Wordnet: A lexical database for English. Communications of the ACM 38(11):39–41, 1995.

    Article  Google Scholar 

  21. Fellbaum, C., Wordnet. Theory and applications of ontology: Computer applications pp 231–243, 2010.

  22. Cilibrasi, R., and Vitanyi, P., The Google similarity distance. IEEE Transactions on Knowledge and Data Engineering 19(3):370–383, 2007.

    Article  Google Scholar 

  23. Fan, J., Shen, Y., Yang, C., and Zhou, N., Structured max-margin learning for inter-related classifier training and multilabel image annotation. IEEE Transactions on Image Processing 20(3):837–854, 2011.

    Article  PubMed  Google Scholar 

  24. Dong, P., Mei, K., Zheng, N., Lei, H., and Fan, J., Training inter-related classifiers for automatic image classification and annotation. Pattern Recognition, Elsevier 46(5):1382–1395, 2012.

    Article  Google Scholar 

  25. Torralba, A., Murphy, K., and Freeman, W., Sharing features: Efficient boosting procedures for multiclass object detection. IEEE computer society conference on computer vision and Pattern Recognition 2:762–769, 2004.

    Google Scholar 

  26. Kuang, Z., Li, Z., Zhao, T., Fan, J., Deep multi-task learning for large-scale image classification. IEEE 3rd International Conference on Multimedia Big Data. doi:10.1109/BigMM.2017.72, 2017.

  27. Chen, T., Lu, S., Fan, J., S-CNN: Subcategory-aware convolutional networks for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence doi:10.1109/TPAMI.2017.2756936, 1, 2017.

  28. Fan, J., Gao, Y., Luo, H., and Jain, R., Mining multilevel image semantics via hierarchical classification. IEEE Transactions on Multimedia 10(2):167–187, 2008. https://doi.org/10.1109/TMM.2007.911775.

    Article  Google Scholar 

  29. Fan, J., Gao, Y., and Luo, H., Integrating concept ontology and multitask learning to achieve more effective classifier training for multilevel image annotation. IEEE Transactions on Image Processing 17(3):407–426, 2008.

    Article  PubMed  Google Scholar 

  30. Marszalek, M., Schmid, C., Constructing category hierarchies for visual recognition. European Conference on Computer Vision, Springer pp 479–491. doi:10.1007/978-3-540-88693-8_35, 2008.

  31. Marszalek, M., Schmid, C., Semantic hierarchies for visual object recognition. IEEE Conference on Computer Vision and Pattern Recognition pp 1–7. doi:10.1109/CVPR.2007.383272, 2007.

  32. Tousch, A., Herbin, S., and Audibert, J., Semantic hierarchies for image annotation: A survey. Pattern Recognition, Elsevier 45(1):333–345, 2011. https://doi.org/10.1016/j.patcog.2011.05.017.

    Article  Google Scholar 

  33. Zhu, S., Jin, D., Liang, Z., Wang, Q., Sun, Y., and Xu, G., Integration of semantic and visual hashing for image retrieval. Journal of Visual Communication & Image Representation 44:229–235, 2016. https://doi.org/10.1016/j.jvcir.2016.08.013.

    Article  Google Scholar 

  34. Zhao, W., Luo, H., Peng, J., and Fan, J., Spatial pyramid deep hashing for large-scale image retrieval. Neurocomputing 243:166–173, 2017. https://doi.org/10.1016/j.neucom.2017.03.021.

    Article  Google Scholar 

  35. Lu, Y., Zhang, L., Liu, J., and Tian, Q., Constructing concept lexica with small semantic gap. IEEE Transaction on Multimedia 12(4):288–299, 2010. https://doi.org/10.1109/TMM.2010.2046292.

    Article  Google Scholar 

  36. Jing, X. Y., Wu, F., Li, Z., Hu, R., and Zhang, D., Multi-label dictionary learning for image annotation. IEEE Transactions on Image Processing 25(6):2712–2725, 2016.

    Article  Google Scholar 

  37. Wang, R., Xie, Y., Yang, J., Xue, L., Hu, M., and Zhang, Q., Large scale automatic image annotation based on convolutional neural network. Journal of Visual Communication & Image Representation 49:213–224, 2017. https://doi.org/10.1016/j.jvcir.2017.07.004.

    Article  Google Scholar 

  38. Dimitrovskia, I., Kocevb, D., Kitanovskia, I., Loskovskaa, S., and Dzeroski, S., Improved medical image modality classification using a combination of visual and textual features. Computerized Medical Imaging and Graphics, Elsevier 39:14–26, 2014. https://doi.org/10.1016/j.compmedimag.2014.06.005.

    Article  Google Scholar 

  39. Arias, J., Martinez-Gomez, J., Gamez, J. A., Seco de Herrera, A. G., and Muller, H., Medical images modality classification using discrete Bayesian networks. Computer Vision and Image Understanding, Elsevier 151:61–71, 2016. https://doi.org/10.1016/j.cviu.2016.04.002.

    Article  Google Scholar 

  40. Tang, Q., Liu, Y., and Liu, H., Medical image classification via multiscale representation learning. Artificial Intelligence in Medicine, Elsevier 79:71–78, 2017. https://doi.org/10.1016/j.artmed.2017.06.009.

    Article  Google Scholar 

  41. Liu, G. H., Li, Z. Y., Zhang, L., and Xu, Y., Image retrieval based on micro-structure descriptor. Pattern Recognition 44(9):2123–2133, 2011. https://doi.org/10.1016/j.patcog.2011.02.003.

    Article  Google Scholar 

  42. Wang, J., Kumar, S., and Chang, S., Semi-supervised hashing for large scale search. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(12):2393–2406, 2012. https://doi.org/10.1109/TPAMI.2012.48.

    Article  PubMed  Google Scholar 

  43. Liong, V. E., Lu, J., Wang, G., Moulin, P., Zhou, J., Deep hashing for compact binary codes learning. IEEE Conference on Computer Vision and Pattern Recognition pp 2475–2483. doi:10.1109/CVPR.2015.7298862, 2015.

  44. Ivasic-Kos, M., Pobar, M., and Ribaric, S., Two-tier image annotation model based on a multi-label classifier and fuzzy-knowledge representation scheme. Elsevier-Pattern Recognition 52:287–305, 2016.

    Article  Google Scholar 

Download references

Acknowledgements

Dr. Sugumaran’s research has been supported by a 2018 School of Business Administration Spring/Summer Research Fellowship from Oakland University.

Author information

Authors and Affiliations

  1. Department of Computer science & Engineering, Easwari Engineering College, Chennai, India

    P. Mercy Rajaselvi Beaulah

  2. Department of Computer science & Engineering, Anna University, Chennai, India

    D. Manjula

  3. Department of Decision and Information Sciences, School of Business Administration, Oakland University, Rochester, MI, 48309, USA

    Vijayan Sugumaran

  4. Center for Data Science and Big Data Analytics, Oakland University, Rochester, MI, 48309, USA

    Vijayan Sugumaran

Authors
  1. P. Mercy Rajaselvi Beaulah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Manjula
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vijayan Sugumaran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Mercy Rajaselvi Beaulah.

Ethics declarations

Conflict of interest

Mercy Rajaselvi Beaulah P. declares that she has no conflict of interest. Manjula D. declares that she has no conflict of interest. Vijayan Sugumaran declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

This article is part of the Topical Collection on Image & Signal Processing

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mercy Rajaselvi Beaulah, P., Manjula, D. & Sugumaran, V. Categorization of Images Using Autoencoder Hashing and Training of Intra Bin Classifiers for Image Classification and Annotation. J Med Syst 42, 132 (2018). https://doi.org/10.1007/s10916-018-0986-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-018-0986-6

Keywords

Search

Navigation