SpringerLink
Log in

Recent advances in scene image representation and classification

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

Abstract

With the rise of deep learning algorithms nowadays, scene image representation methods have achieved a significant performance boost, particularly in accuracy, in classification. However, the performance is still limited because the scene images are mostly complex having higher intra-class dissimilarity and inter-class similarity problems. To deal with such problems, there have been several methods proposed in the literature with their advantages and limitations. A detailed study of previous works is necessary to understand their advantages and disadvantages in image representation and classification problems. In this paper, we review the existing scene image representation methods that are being widely used for image classification. For this, we, first, devise the taxonomy using the seminal existing methods proposed in the literature to this date using deep learning (DL)-based, computer vision (CV)-based, and search engine (SE)-based methods. Next, we compare their performance both qualitatively (e.g., quality of outputs, pros/cons, etc.) and quantitatively (e.g., accuracy). Last, we speculate on the prominent research directions in scene image representation tasks using keyword growth and timeline analysis. Overall, this survey provides in-depth insights and applications of recent scene image representation methods under three different methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Data Availability

All data are publicly available.

References

  1. Ali N, Zafar B, Riaz F, Dar SH, Ratyal NI, Bajwa KB, Iqbal MK, Sajid M (2018) A hybrid geometric spatial image representation for scene classification. PloS ONE 13(9):e0203,339

    Google Scholar 

  2. Anu E, Anu K (2016) A survey on scene recognition. Int J Sci Eng Technol Res(IJSETR) 5:64–68

    Google Scholar 

  3. Aria M, Cuccurullo C (2017) Bibliometrix: an r-tool for comprehensive science map** analysis. J Informetrics 11(4):959–975

    Google Scholar 

  4. Ayalew AM, Salau AO, Abeje BT, Enyew B (2022) Detection and classification of covid-19 disease from x-ray images using convolutional neural networks and histogram of oriented gradients. Biomed Signal Process Control 74:103,530

    Google Scholar 

  5. Bai S (2017) Growing random forest on deep convolutional neural networks for scene categorization. Expert Syst Appl 71:279–287

    Google Scholar 

  6. Bai S, Tang H, An S (2019) Coordinate cnns and lstms to categorize scene images with multi-views and multi-levels of abstraction. Expert Syst Appl 120:298–309

    Google Scholar 

  7. Banerji S, Sinha A, Liu C (2012) Novel color, shape and texture-based scene image descriptors. In: 2012 IEEE 8th International conference on intelligent computer communication and processing, IEEE, pp 245–248

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

    Google Scholar 

  9. Bosch A, Zisserman A, Muñoz X (2008) Scene classification using a hybrid generative/discriminative approach. IEEE Trans Pattern Anal Mach Intell 30(4):712–727

    Google Scholar 

  10. Chen G, Song X, Zeng H, Jiang S (2020) Scene recognition with prototype-agnostic scene layout. IEEE Trans Image Process 29:5877–5888

    Google Scholar 

  11. Chen H, **e K, Wang H, Zhao C (2018) Scene image classification using locality-constrained linear coding based on histogram intersection. Multimed Tools Appl 77(3):4081–4092

    Google Scholar 

  12. Cheng X, Lu J, Feng J, Yuan B, Zhou J (2018) Scene recognition with objectness. Pattern Recognit 74:474–487

    Google Scholar 

  13. Cho WS, Lam KM (2012) An efficient and effective hybrid pyramid kernel for un-segmented image classification. In: 2012 International conference on systems and informatics (ICSAI2012), IEEE, pp 2153–2158

  14. Choe S, Seong H, Kim E (2021) Indoor place category recognition for a cleaning robot by fusing a probabilistic approach and deep learning. IEEE Transactions on Cybernetics

  15. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proc. ieee comput. soc. conf. comput. vis. pattern recognit. (CVPR), pp 886–893

  16. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. CVPR

  17. Dixit M, Chen S, Gao D, Rasiwasia N, Vasconcelos N (2015) Scene classification with semantic fisher vectors. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp 2974–2983

  18. Dixit M, Li Y, Vasconcelos N (2019) Semantic fisher scores for task transfer: using objects to classify scenes. IEEE Trans Pattern Anal Mach Intell 42 (12):3102–3118

    Google Scholar 

  19. Dutta R, Aryal J, Das A, Kirkpatrick JB (2013) Deep cognitive imaging systems enable estimation of continental-scale fire incidence from climate data. Sci Rep 3(1):1–4

    Google Scholar 

  20. Fei-Fei L, Perona P (2005) A Bayesian hierarchical model for learning natural scene categories. In: Proc. IEEE comput. Soc. Conf. Comput. Vis. and pattern recognit. (CVPR), vol 2, pp 524–531

  21. Fornoni M, Caputo B (2014) Scene recognition with naive bayes non-linear learning. In: 2014 22nd International conference on pattern recognition, IEEE, pp 3404–3409

  22. Gong Y, Wang L, Guo R, Lazebnik S (2014) Multi-scale orderless pooling of deep convolutional activation features. In: Proc. Eur. Conf. Comput. Vis. (ECCV), pp 392–407

  23. Griffin G, Holub A, Perona P (2007) Caltech-256 object category dataset

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

    MathSciNet  Google Scholar 

  25. Guo Y, Lew MS (2016) Bag of Surrogate parts: one inherent feature of deep cnns. In: Proc. of the British Machine Vision Conference (BMVC)

  26. Gupta S, Dileep AD, Thenkanidiyoor V (2021) Recognition of varying size scene images using semantic analysis of deep activation maps. Mach Vis Appl 32(2):1–19

    Google Scholar 

  27. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc. IEEE conf. comput. vis. pattern recognit. (CVPR), pp 770–778

  28. Hu J, Guo P (2012) Spatial local binary patterns for scene image classification. In: 2012 6Th international conference on sciences of electronics, technologies of information and telecommunications, SETIT, IEEE, pp 326–330

  29. Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. In: Proc. 22nd ACM Int. Conf. on Multimedia (ACMM), pp 675–678

  30. Jiang S, Chen G, Song X, Liu L (2019) Deep patch representations with shared codebook for scene classification. ACM Trans Multimed Comput Commun Appl 15(1s):1–17

    Google Scholar 

  31. Juneja M, Vedaldi A, Jawahar C, Zisserman A (2013) Blocks that shout: distinctive parts for scene classification. In: Proc. IEEE conf. comput. vis. pattern recognit. (CVPR), pp 923–930

  32. Khan A, Chefranov A, Demirel H (2021) Image scene geometry recognition using low-level features fusion at multi-layer deep cnn. Neurocomputing 440:111–126

    Google Scholar 

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

    MathSciNet  Google Scholar 

  34. Kim Y (2014) Convolutional neural networks for sentence classification. ar**v:14085882

  35. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proc. adv. neural inf. process. syst. (NIPS), pp 1097–1105

  36. Kuzborskij I, Maria Carlucci F, Caputo B (2016) When naive bayes nearest neighbors meet convolutional neural networks. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp 2100–2109

  37. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proc. IEEE comput. soc. conf. comput. vis. pattern recognit. (CVPR), pp 2169–2178

  38. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Google Scholar 

  39. Li LJ, Li FF (2007) What, where and who? classifying events by scene and object recognition. In: Proc. 11th int. Conf. Comput. Vis. (ICCV), vol 2, p 6

  40. Li Q, Qin Z, Chai L, Zhang H, Guo J, Bhanu B (2013) Representative reference-set and betweenness centrality for scene image categorization. In: 2013 IEEE International conference on image processing, IEEE, pp 3254–3258

  41. Li LJ, Su H, Fei-Fei L, **ng EP (2010) Object bank: a high-level image representation for scene classification & semantic feature sparsification. In: Proc. adv. neural inf. process. syst. (NIPS), pp 1378–1386

  42. Li Q, Zhang H, Guo J, Bhanu B, An L (2012) Reference-based scheme combined with k-svd for scene image categorization. IEEE Signal Process Lett 20(1):67–70

    Google Scholar 

  43. Lin C, Lee F, Cai J, Chen H, Chen Q (2021) Global and graph encoded local discriminative region representation for scene recognition. Comput Model Eng Sci 128(3):985–1006

    Google Scholar 

  44. Lin D, Lu C, Liao R, Jia J (2014) Learning important spatial pooling regions for scene classification. In: Proc. IEEE conf. comput. vis. pattern recognit. (CVPR), pp 3726–3733

  45. Lin TY, RoyChowdhury A, Maji S (2015) Bilinear cnn models for fine-grained visual recognition. In: Proc. IEEE int. conf. comput. vis. (ICCV), pp 1449–1457

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

    Google Scholar 

  47. Liu W, Li Y, Wu Q (2018) An attribute-based high-level image representation for scene classification. IEEE Access 7:4629–4640

    Google Scholar 

  48. Liu S, Tian G (2019) An indoor scene classification method for service robot based on cnn feature. J Robot 2019

  49. Liu S, Tian G, Xu Y (2019) A novel scene classification model combining resnet based transfer learning and data augmentation with a filter. Neurocomputing 338:191–206

    Google Scholar 

  50. Liu S, Tian G, Zhang Y, Duan P (2021) Scene recognition mechanism for service robot adapting various families: a cnn-based approach using multi-type cameras. IEEE Trans Multimed 24:2392–2406

    Google Scholar 

  51. Lopez-Cifuentes A, Escudero-Vinolo M, Bescos J, Garcia-Martin A (2020) Semantic-aware scene recognition. Pattern Recognit 102:107,256

    Google Scholar 

  52. Lowe DG (1999) Object recognition from local scale-invariant features, vol 2, pp 1150–1157

  53. Margolin R, Zelnik-Manor L, Tal A (2014) OTC: a novel local descriptor for scene classification. In: Proc. Eur. Conf. Comput. Vis. (ECCV), pp 377–391

  54. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133

    MathSciNet  Google Scholar 

  55. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. ar**v:13013781

  56. Moller T, Machiraju R, Mueller K, Yagel R (1997) Evaluation and design of filters using a taylor series expansion. IEEE Trans Vis Comput Graph 3(2):184–199

    Google Scholar 

  57. Nascimento G, Laranjeira C, Braz V, Lacerda A, Nascimento ER (2017) A robust indoor scene recognition method based on sparse representation. ar**v:1708.07555

  58. Neupane B, Horanont T, Aryal J (2021) Deep learning-based semantic segmentation of urban features in satellite images: a review and meta-analysis. Remote Sens 13(4):808

    Google Scholar 

  59. Niu Z, Zhou Y, Shi K (2010) A hybrid image representation for indoor scene classification. In: 2010 25th International conference of image and vision computing New Zealand, IEEE, pp 1–7

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

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

    Google Scholar 

  62. Parizi N, Oberlin JG, Felzenszwalb PF (2012) Reconfigurable models for scene recognition. In: Proc. comput. vis. pattern recognit. (CVPR), pp 2775–2782

  63. Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proc. conf. on empirical methods in natural language processing (EMNLP), pp 1532–1543

  64. Perronnin F, Sanchez J, Mensink T (2010) Improving the fisher kernel for large-scale image classification. In: Proc. Eur. Conf. Comput. Vis. (ECCV), pp 143–156

  65. Qi M, Wang Y (2016) Deep-cssr: scene classification using category-specific salient region with deep features. In: 2016 IEEE International Conference on Image Processing (ICIP), IEEE, pp 1047–1051

  66. Quattoni A, Torralba A (2009) Recognizing indoor scenes. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp 413–420

  67. Rasiwasia N, Vasconcelos N (2008) Scene classification with low-dimensional semantic spaces and weak supervision. In: IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp 1–6

  68. Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263–7271

  69. Ren X, Bo L, Fox D (2012) Rgb-(d) scene labeling: features and algorithms. In: 2012 IEEE Conference on computer vision and pattern recognition, IEEE, pp 2759–2766

  70. Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303–304

    Google Scholar 

  71. Sȧnchez J, Perronnin F, Mensink T, Verbeek J (2013) Image classification with the fisher vector: theory and practice. Int J Comput Vis 105(3):222–245

    MathSciNet  Google Scholar 

  72. Shadman Roodposhti M, Aryal J, Lucieer A, Bryan BA (2019) Uncertainty assessment of hyperspectral image classification: deep learning vs. random forest. Entropy 21(1):78

    MathSciNet  Google Scholar 

  73. Shahi TB, Sitaula C (2021) Natural language processing for nepali text: a review. Artif Intell Rev 1–29

  74. Shahi TB, Sitaula C, Neupane A, Guo W (2022) Fruit classification using attention-based mobilenetv2 for industrial applications. PloS ONE 17(2):e0264,586

    Google Scholar 

  75. Sharma K, Gupta S, Dileep AD, Rameshan R (2018) Scene image classification using reduced virtual feature representation in sparse framework. In: 2018 IEEE International conference on acoustics, speech and signal processing, ICASSP, IEEE, pp 2701–2705

  76. ShenghuaGao IH, Liang-TienChia P (2010) Local features are not lonely–Laplacian sparse coding for image classification. In: Proc. IEEE conf. comput. vis. pattern recognit. (CVPR), pp 3555–3561

  77. Silberman N, Fergus R (2011) Indoor scene segmentation using a structured light sensor. In: 2011 IEEE International conference on computer vision workshops, ICCV workshops, IEEE, pp 601–608

  78. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. ar**v:1409.1556

  79. Singh V, Girish D, Ralescu A (2017) Image understanding-a brief review of scene classification and recognition. In: Proc. modern artificial intelligence and cognitive science (MAICS), pp 85–91

  80. Sinha A, Banerji S, Liu C (2012) Novel gabor-phog features for object and scene image classification. In: Joint IAPR international workshops on statistical techniques in pattern recognition (SPR) and structural and syntactic pattern recognition, SSPR, Springer, pp 584–592

  81. Sinha A, Banerji S, Liu C (2014) Scene image classification using a wigner-based local binary patterns descriptor. In: 2014 International joint conference on neural networks, IJCNN, IEEE, pp 1614–1621

  82. Sinha A, Banerji S, Liu C (2014) New color gphog descriptors for object and scene image classification. Mach Vis Appl 25(2):361–375

    Google Scholar 

  83. Sitaula C, Aryal S, **ang Y, Basnet A, Lu X (2021b) Content and context features for scene image representation. Knowledge-Based Systems 107470

  84. Sitaula C, Basnet A, Mainali A, Shahi T (2021) Deep learning-based methods for sentiment analysis on nepali covid-19-related tweets. Computational Intelligence and Neuroscience 2021

  85. Sitaula C, Shahi TB (2022) Monkeypox virus detection using pre-trained deep learning-based approaches. J Med Syst 46(11):1–9

    Google Scholar 

  86. Sitaula C, **ang Y, Aryal S, Lu X (2019) Unsupervised deep features for privacy image classification. In: Proc. pacific-rim symposium on image and video technology (PSIVT), pp 404–415

  87. Sitaula C, **ang Y, Aryal S, Lu X (2021a) Scene image representation by foreground, background and hybrid features. Expert Systems with Applications 115285

  88. Sitaula C, **ang Y, Basnet A, Aryal S, Lu X (2019) Tag-based semantic features for scene image classification. In: Proc. int. conf. on neural inf. process. (ICONIP), pp 90–102

  89. Sitaula C, **ang Y, Basnet A, Aryal S, Lu X (2020) HDF: hybrid deep features for scene image representation. In: Proc. int. joint conf. on neural networks (IJCNN

  90. Sitaula C, **ang Y, Zhang Y, Lu X, Aryal S (2019) Indoor image representation by high-level semantic features. IEEE Access 7:84,967–84,979

    Google Scholar 

  91. Sorkhi AG, Hassanpour H, Fateh M (2020) A comprehensive system for image scene classification. Multimed Tools Appl 1–26

  92. Sun N, Li W, Liu J, Han G, Wu C (2018) Fusing object semantics and deep appearance features for scene recognition. IEEE Trans Circ Syst Video Technol 29(6):1715–1728

    Google Scholar 

  93. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2014) Going deeper with convolutions. In: Proc. IEEE conf. comput. vis. pattern recognit. (CVPR), pp 1–9

  94. Tang P, Wang H, Kwong S (2017) G-ms2f: Googlenet based multi-stage feature fusion of deep cnn for scene recognition. Neurocomputing 225:188–197

    Google Scholar 

  95. Van Gemert JC, Veenman CJ, Smeulders AW, Geusebroek JM (2009) Visual word ambiguity. IEEE Trans Pattern Anal Mach Intell 32(7):1271–1283

    Google Scholar 

  96. Wang D, Mao K (2019) Learning semantic text features for web text-aided image classification. IEEE Trans Multimed 21(12):2985–2996

    Google Scholar 

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

    Google Scholar 

  98. Wang C, Peng G, De Baets B (2022) Joint global metric learning and local manifold preservation for scene recognition. Inf Sci 610:938–956

    Google Scholar 

  99. Wang J, Wang W, Wang R, Gao W (2016) Csps: an adaptive pooling method for image classification. IEEE Trans Multimed 18(6):1000–1010

    MathSciNet  Google Scholar 

  100. Wang Z, Wang L, Wang Y, Zhang B, Qiao Y (2017) Weakly supervised patchnets: describing and aggregating local patches for scene recognition. IEEE Trans Image Process 26(4):2028–2041

    MathSciNet  Google Scholar 

  101. Wei X, Phung SL, Bouzerdoum A (2016) Visual descriptors for scene categorization: experimental evaluation. Artif Intell Rev 45(3):333–368

    Google Scholar 

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

    Google Scholar 

  103. Wu R, Wang B, Wang W, Yu Y (2015) Harvesting discriminative meta objects with deep cnn features for scene classification. In: Proceedings of the IEEE international conference on computer vision, pp 1287–1295

  104. **ao J, Hays J, Ehinger KA, Oliva A, Torralba A (2010) Sun database: large-scale scene recognition from abbey to zoo. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp 3485–3492

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

    MathSciNet  Google Scholar 

  106. **e GS, ** XB, Zhang XY, Zang SF, Yang C, Wang Z, Pu J (2018) From class-specific to class-mixture: cascaded feature representations via restricted boltzmann machine learning. IEEE Access 6:69,393–69,406

    Google Scholar 

  107. **e L, Lee F, Liu L, Kotani K, Chen Q (2020) Scene recognition: a comprehensive survey. Pattern Recognit 107205

  108. **e L, Lee F, Liu L, Yin Z, Yan Y, Wang W, Zhao J, Chen Q (2018) Improved spatial pyramid matching for scene recognition. Pattern Recogn 82:118–129

    Google Scholar 

  109. Yang S, Ramanan D (2015) Multi-scale recognition with DAG-CNNs. In: Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp 1215–1223

  110. Zabih R, Woodfill J (1994) Non-parametric local transforms for computing visual correspondence. In: Proc. euro. conf. comput. vis. (ECCV), pp 151–158

  111. Zeglazi O, Amine A, Rziza M (2016) Sift descriptors modeling and application in texture image classification. In: Proc. 13th int. Conf. Comput, Graphics, Imaging and Visualization (CGiV), pp 265–268

  112. Zhang C, Cheng J, Liu J, Pang J, Liang C, Huang Q, Tian Q (2014) Object categorization in sub-semantic space. Neurocomputing 142:248–255

    Google Scholar 

  113. Zhang C, Liu J, Tian Q, Liang C, Huang Q (2013) Beyond visual features: a weak semantic image representation using exemplar classifiers for classification. Neurocomputing 120:318–324

    Google Scholar 

  114. Zhang B, Wang Q, Lu X, Wang F, Li P (2020) Locality-constrained affine subspace coding for image classification and retrieval. Pattern Recognit 100:107,167

    Google Scholar 

  115. Zhang L, Zhen X, Shao L (2014) Learning object-to-class kernels for scene classification. IEEE Trans Image Process 23(8):3241–3253

    MathSciNet  Google Scholar 

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

    Google Scholar 

  117. Zhou B, Khosla A, Lapedriza A, Torralba A, Oliva A (2016) Places: an image database for deep scene understanding. ar**v:161002055

  118. Zhou B, Lapedriza A, Khosla A, Oliva A, Torralba A (2017) Places: a 10 million image database for scene recognition. IEEE Trans Pattern Anal Mach Intell 40(6):1452–1464

    Google Scholar 

  119. Zhu J, Li Lj, Fei-Fei L, **ng EP (2010) Large margin learning of upstream scene understanding models. In: Proc. Adv. Neural Inf. Process. Syst. (NIPS), pp 2586–2594

  120. Zuo Z, Wang G, Shuai B, Zhao L, Yang Q (2015) Exemplar based deep discriminative and shareable feature learning for scene image classification. Pattern Recogn 48(10):3004–3015

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Systems Engineering, Monash University, Wellington Rd, Clayton, VIC, 3800, Australia

    Chiranjibi Sitaula & Faezeh Marzbanrad

  2. School of Engineering and Technology, Central Queensland University, Rockhampton, QLD, 4701, Australia

    Tej Bahadur Shahi

  3. Central Department of Computer Science and Information Technology (CDCSIT), Tribhuvan University, TU Rd, Kirtipur, 44618, Kathmandu, Nepal

    Tej Bahadur Shahi

  4. Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC, 3010, Australia

    Jagannath Aryal

Authors
  1. Chiranjibi Sitaula
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tej Bahadur Shahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Faezeh Marzbanrad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jagannath Aryal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chiranjibi Sitaula.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sitaula, C., Shahi, T.B., Marzbanrad, F. et al. Recent advances in scene image representation and classification. Multimed Tools Appl 83, 9251–9278 (2024). https://doi.org/10.1007/s11042-023-15005-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15005-9

Keywords

Search

Navigation