Skip to main content
SpringerLink
Log in

Tag-Based Semantic Features for Scene Image Classification

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11955))

Included in the following conference series:

Abstract

The existing image feature extraction methods are primarily based on the content and structure information of images, and rarely consider the contextual semantic information. Regarding some types of images such as scenes and objects, the annotations and descriptions of them available on the web may provide reliable contextual semantic information for feature extraction. In this paper, we introduce novel semantic features of an image based on the annotations and descriptions of its similar images available on the web. Specifically, we propose a new method which consists of two consecutive steps to extract our semantic features. For each image in the training set, we initially search the top k most similar images from the internet and extract their annotations/descriptions (e.g., tags or keywords). The annotation information is employed to design a filter bank for each image category and generate filter words (codebook). Finally, each image is represented by the histogram of the occurrences of filter words in all categories. We evaluate the performance of the proposed features in scene image classification on three commonly-used scene image datasets (i.e., MIT-67, Scene15 and Event8). Our method typically produces a lower feature dimension than existing feature extraction methods. Experimental results show that the proposed features generate better classification accuracies than vision based and tag based features, and comparable results to deep learning based features.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.yandex.com/images/.

  2. 2.

    https://scikit-learn.org/stable/.

References

  1. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)

    Article  Google Scholar 

  2. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 886–893 (2005)

    Google Scholar 

  3. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings IEEE Conference Computer Vision and Pattern Recognition (CVPR) (2009)

    Google Scholar 

  4. Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 524–531, June 2005

    Google Scholar 

  5. Gong, Y., Wang, L., Guo, R., Lazebnik, S.: Multi-scale orderless pooling of deep convolutional activation features. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 392–407. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10584-0_26

    Chapter  Google Scholar 

  6. Guo, S., Huang, W., Wang, L., Qiao, Y.: Locally supervised deep hybrid model for scene recognition. IEEE Trans. Image Process. 26(2), 808–820 (2017)

    Article  MathSciNet  Google Scholar 

  7. Guo, Y., Liu, Y., Lao, S., Bakker, E.M., Bai, L., Lew, M.S.: Bag of surrogate parts feature for visual recognition. IEEE Trans. Multimedia 20(6), 1525–1536 (2018)

    Article  Google Scholar 

  8. Hartigan, J.A.: Clustering Algorithms, 99th edn. Wiley, New York (1975)

    MATH  Google Scholar 

  9. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

    Google Scholar 

  10. Hearst, M.A.: Support vector machines. IEEE Intell. Syst. 13(4), 18–28 (1998)

    Article  Google Scholar 

  11. Huang, A.: Similarity measures for text document clustering. In: Proceedings of Sixth New Zealand Computer Science Research Student Conference (NZCSRSC 2008), pp. 9–56 (2008)

    Google Scholar 

  12. Juneja, M., Vedaldi, A., Jawahar, C., Zisserman, A.: Blocks that shout: distinctive parts for scene classification. In: Proceedings of IEEE Conference Computer Vision Pattern Recognition (CVPR), pp. 923–930, June 2013

    Google Scholar 

  13. Khan, S.H., Hayat, M., Bennamoun, M., Togneri, R., Sohel, F.A.: A discriminative representation of convolutional features for indoor scene recognition. IEEE Trans. Image Process. 25(7), 3372–3383 (2016)

    Article  MathSciNet  Google Scholar 

  14. Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)

  15. Kuzborskij, I., Maria Carlucci, F., Caputo, B.: When Naive Bayes nearest neighbors meet convolutional neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2100–2109 (2016)

    Google Scholar 

  16. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of IEEE Computer Society Conference on Computer Vision Pattern Recognition, pp. 2169–2178, June 2006

    Google Scholar 

  17. Li, L.J., Li, F.F.: What, where and who? Classifying events by scene and object recognition. In: ICCV, vol. 2, p. 6 (2007)

    Google Scholar 

  18. Li, L.J., Su, H., Fei-Fei, L., Xing, E.P.: Object bank: a high-level image representation for scene classification & semantic feature sparsification. In: Proceedings of Advance Neural Information Processing Systems (NIPS), pp. 1378–1386 (2010)

    Google Scholar 

  19. Lin, D., Lu, C., Liao, R., Jia, J.: Learning important spatial pooling regions for scene classification. In: Proceedings of IEEE Conference Computer Vision Pattern Recognition (CVPR), pp. 3726–3733, June 2014

    Google Scholar 

  20. Lin, T.Y., RoyChowdhury, A., Maji, S.: Bilinear convolutional neural networks for fine-grained visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(6), 1309–1322 (2018)

    Article  Google Scholar 

  21. Margolin, R., Zelnik-Manor, L., Tal, A.: OTC: a novel local descriptor for scene classification. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 377–391. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10584-0_25

    Chapter  Google Scholar 

  22. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  23. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  24. Oliva, A.: Gist of the scene. In: Neurobiology of Attention, pp. 251–256. Elsevier (2005)

    Google Scholar 

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

    Article  Google Scholar 

  26. Parizi, N., Oberlin, J.G., Felzenszwalb, P.F.: Reconfigurable models for scene recognition. In: Proceedings of Computer Vision Pattern Recognition (CVPR), pp. 2775–2782, June 2012

    Google Scholar 

  27. Patel, A., Sands, A., Callison-Burch, C., Apidianaki, M.: Magnitude: a fast, efficient universal vector embedding utility package. In: Proceedings Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 120–126 (2018)

    Google Scholar 

  28. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  29. Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_11

    Chapter  Google Scholar 

  30. Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: Proceedings of IEEE Conference Computer Vision Pattern Recognition (CVPR), pp. 413–420, June 2009

    Google Scholar 

  31. ShenghuaGao, I.H., Liang-TienChia, P.: Local features are not lonely-Laplacian sparse coding for image classification, pp. 3555–3561 (2010)

    Google Scholar 

  32. Sitaula, C., Xiang, Y., Aryal, S., Lu, X.: Unsupervised deep features for privacy image classification. arXiv preprint arXiv:1909.10708 (2019)

    Chapter  Google Scholar 

  33. Sitaula, C., Xiang, Y., Zhang, Y., Lu, X., Aryal, S.: Indoor image representation by high-level semantic features. IEEE Access 7, 84967–84979 (2019)

    Article  Google Scholar 

  34. Tang, P., Wang, H., Kwong, S.: G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition. Neurocomputing 225, 188–197 (2017)

    Article  Google Scholar 

  35. Wang, D., Mao, K.: Task-generic semantic convolutional neural network for web text-aided image classification. Neurocomputing 329, 103–115 (2019)

    Article  Google Scholar 

  36. Wang, D., Mao, K.: Learning semantic text features for web text aided image classification. IEEE Trans. Multimedia PP, 1 (2019)

    Google Scholar 

  37. Wu, J., Rehg, J.M.: CENTRIST: a visual descriptor for scene categorization. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1489–1501 (2011)

    Article  Google Scholar 

  38. Xiao, Y., Wu, J., Yuan, J.: mCENTRIST: a multi-channel feature generation mechanism for scene categorization. IEEE Trans. Image Process. 23(2), 823–836 (2014)

    Article  MathSciNet  Google Scholar 

  39. Zeglazi, O., Amine, A., Rziza, M.: Sift descriptors modeling and application in texture image classification. In: Proceedings of 13th International Conference Computer Graphics, Imaging and Visualization (CGiV), pp. 265–268, March 2016

    Google Scholar 

  40. Zhang, C., Zhu, G., Huang, Q., Tian, Q.: Image classification by search with explicitly and implicitly semantic representations. Inf. Sci. 376, 125–135 (2017)

    Article  Google Scholar 

  41. Zhou, B., Khosla, A., Lapedriza, A., Torralba, A., Oliva, A.: Places: An image database for deep scene understanding. arXiv preprint arXiv:1610.02055 (2016)

  42. Zhu, J., Li, L.J., Fei-Fei, L., Xing, E.P.: Large margin learning of upstream scene understanding models. In: Proceedings of Advance Neural Information Processing Systems (NIPS), pp. 2586–2594 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Technology, Deakin University, Geelong, Australia

    Chiranjibi Sitaula, Yong Xiang, Sunil Aryal & Xuequan Lu

  2. Ambition College, Kathmandu, Nepal

    Anish Basnet

Authors
  1. Chiranjibi Sitaula
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anish Basnet
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sunil Aryal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xuequan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chiranjibi Sitaula .

Editor information

Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sitaula, C., Xiang, Y., Basnet, A., Aryal, S., Lu, X. (2019). Tag-Based Semantic Features for Scene Image Classification. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11955. Springer, Cham. https://doi.org/10.1007/978-3-030-36718-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36718-3_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36717-6

  • Online ISBN: 978-3-030-36718-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation