Skip to main content
SpringerLink
Log in

Deep CNN based online image deduplication technique for cloud storage system

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

Abstract

Online image detection is one of the most critical components of an image deduplication technique for an efficient cloud storage system. Although extensive research has been conducted in this field, the problem still remains challenging. Deep learning techniques have achieved significant success in solving a variety of computer vision issues and have high potential in image deduplication techniques. Deduplication is an efficient method in a cloud storage system that minimizes redundant data at the file or sub-file level using cryptographic hash signatures. Although significant research on offline image deduplication techniques have been reported, yet limited research is available on online image deduplication techniques. Online image matching accuracy and performance has been a major challenge for online image deduplication techniques to detect exact or near-exact images using feature extraction techniques. These first use feature extraction techniques to extract image features and then match these image features to detect duplicate images. In this paper, we have proposed a Deep CNN based online image deduplication technique for a cloud storage system to detect exact and near-exact images using cross-domains, even in the presence of perturbations in the form of blur, noise, compression, lighting variations and many more. The experimental results show that our proposed deep CNN for online image deduplication technique outperforms in terms of image matching accuracy and performance. The paper also proposed a Hot Decomposition Vector (HDV) for image patch generation to store efficiently dissimilar parts of near-exact images. The experimental results demonstrate that HDV exhibits higher and stable image matching accuracy in all three types of image deformations with relatively small computation time.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Alkawaz MH, Sulong G, Saba T, Rehman A (2018) Detection of copy-move image forgery based on discrete cosine transform. Neural Comput & Applic 30(1):183–192. https://doi.org/10.1007/s00521-016-2663-3

    Article  Google Scholar 

  2. Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53:50–58

    Article  Google Scholar 

  3. Banerji S, Sinha A, Liu C (2013) New image descriptors based on color, texture, shape, and wavelets for object and scene image classification. Neurocomputing 117:173–185. https://doi.org/10.1016/j.neucom.2013.02.014

    Article  Google Scholar 

  4. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110(3):346–359. https://doi.org/10.1016/j.cviu.2007.09.014

    Article  Google Scholar 

  5. Biadgie Y, Sohn KA (2014) Feature detector using adaptive accelerated segment test. In: 2014 international conference on information science and applications (ICISA) 2014, (pp. 1-4). IEEE. https://doi.org/10.1109/ICISA.2014.6847403

  6. Chen CC, Hsieh SL (2015) Using binarization and hashing for efficient SIFT matching. J Vis Commun Image Represent 30:86–93. https://doi.org/10.1016/j.jvcir.2015.02.014

    Article  Google Scholar 

  7. Chen M, Wang Y, Zou X, Wang S, Wu G (2012) A duplicate image deduplication approach via Haar wavelet technology. In: 2nd International Conference on Cloud Computing and Intelligence Systems, IEEE 2: 624–628. https://doi.org/10.1109/CCIS.2012.6664249

  8. Chen M, Wang S, Tian L (2013) A high-precision duplicate image deduplication approach. J Comput 8(11):2768–2775. https://doi.org/10.4304/jcp.8.11.2768-2775

    Article  Google Scholar 

  9. Dharshini T, Angelina JJR (2019) Review and analysis of image and video deduplication techniques. J Inf Comput Sci 9(12)

  10. Ding C, Tao D (2015) Robust face recognition via multimodal deep face representation. IEEE Trans Multimedia 17(11):2049–2058. https://doi.org/10.1109/TMM.2015.2477042

    Article  Google Scholar 

  11. Diwakar M, Kumar M (2018) A review on CT image noise and its denoising. Biomed Signal Process Control 42:73–88. https://doi.org/10.1016/j.bspc.2018.01.010

    Article  Google Scholar 

  12. Diwakar M, Kumar M (2018) CT image denoising using NLM and correlation-based wavelet packet thresholding. IET Image Process 12(5):708–715

    Article  Google Scholar 

  13. Diwakar M, Singh P (2010) CT image denoising using multivariate model and its method noise thresholding in non-subsampled shearlet domain. Biomed Signal Process Control 57:101754. https://doi.org/10.1016/j.bspc.2019.101754

    Article  Google Scholar 

  14. Foo JJ, Sinha R, Zobel J (2007) SICO: a system for detection of near-duplicate images during search. Multimedia and Expo, IEEE International Conference on, 595–598. https://doi.org/10.1109/ICME.2007.4284720

  15. Gang H, Yan H, Xu L (2015) Secure image deduplication in cloud storage. In: Khalil I, Neuhold E, Tjoa A, Xu L, You I (eds) Information and Communication Technology ICT-EurAsia, Lecture Notes in Computer Science, vol 9357. Springer, pp 243–251. https://doi.org/10.1007/978-3-319-24315-3_25

    Chapter  Google Scholar 

  16. Girshick R, Donahue J, Darrell T, Malik J (2016) Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans Pattern Anal Mach Intell 38(1):142–158. https://doi.org/10.1109/TPAMI.2015.2437384

    Article  Google Scholar 

  17. Grabner M, Grabner H, Bischof H (2006) Fast approximated SIFT. Lecture Notes in Computer Science, LNCS. In: Asian conference on computer vision, Springer, Berlin, 3851: 918–927. https://doi.org/10.1007/11612032_92

  18. Harris C, Stephens M (1988) A combined corner and edge detector. Procedings of the Alvey Vision Conference 1988, 23.1-23.6. https://doi.org/10.5244/C.2.23

  19. Hua Y, He W, Liu X, Feng D (2015) SmartEye: real-time and efficient cloud image sharing for disaster environments. IEEE Conference on Computer Communications (INFOCOM) 1616-1624, https://doi.org/10.1109/INFOCOM.2015.7218541

  20. Huang F, Zhou Z, Yang CN, Liu X, Wang T (2019) Original image tracing with image relational graph for near-duplicate image elimination. Int J Comput Sci Eng 18:294–304. https://doi.org/10.1504/IJCSE.2019.098540

    Article  Google Scholar 

  21. 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: Proceedings of the 22nd ACM international conference on Multimedia ACM, pp. 675-678. https://doi.org/10.1145/2647868.2654889

  22. Juan L, Gwun O (2009) A comparison of sift, pca-sift and surf. Int J Image Process (IJIP) 3(4):143–152

    Google Scholar 

  23. Jung HM, Park WV, Lee WY, Lee JG, Ko YW (2011) Data deduplication system for supporting multi-mode. In Asian Conference on Intelligent Information and Database Systems, Lecture Notes in Computer Science, Springer 6591:78-87. https://doi.org/10.1007/978-3-642-20039-7-8

  24. Jyoti RR (2019) A hybrid approach for effective image deduplication using PCA, SPIHT and compressive sensing. Women Institute of Technology Conference on Electrical and Computer Engineering (WITCON ECE) 129-134. https://doi.org/10.1109/WITCONECE48374.2019.9092894

  25. Kalia R, Lee KD, Samir BVR, Je SK, Oh WG (2011) An analysis of the effect of different image preprocessing techniques on the performance of SURF: speeded up robust features. 17th Korea-Japan joint workshop on Frontiers of computer vision, FCV 2011. https://doi.org/10.1109/FCV.2011.5739756

  26. Kaur R, Chana I, Bhattacharya J (2018) Data deduplication techniques for efficient cloud storage management: a systematic review. J Supercomput 74(5):2035–2085. https://doi.org/10.1007/s11227-017-2210-8

    Article  Google Scholar 

  27. Ke Y, Sukthankar R (2004) PCA-SIFT: “a more distinctive representation for local image descriptors, in proc. Conf. Computer vision and Pattern Recognition, pp. 511-517, 2004. https://doi.org/10.1109/CVPR.2004.1315206

  28. Ke Y, Sukthankar R, Huston L (2004) Efficient near-duplicate detection and sub-image retrieval. In Proceedings of ACM International Conference on Multimedia (MM), 4(1).

  29. Kordopatis-Zilos G, Papadopoulos S, Patras I, Kompatsiaris I (2019) Finding near-duplicate videos in large-scale collections. In: Mezaris V, Nixon L, Papadopoulos S, Teyssou D (eds) Video Verification in the Fake News Era. Springer. https://doi.org/10.1007/978-3-030-26752-0_4

    Chapter  Google Scholar 

  30. Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. Computer science department, University of Toronto. Tech Rep 1(4):7

    Google Scholar 

  31. Kumar M, Diwakar M (2018) CT image denoising using locally adaptive shrinkage rule in tetrolet domain. J King Saud Unive-Comput Info Sci 30(1):41–50. https://doi.org/10.1016/j.jksuci.2016.03.003

    Article  Google Scholar 

  32. Kumar M, Diwakar M (2019) A new exponentially directional weighted function based CT image denoising using total variation. J King Saud Univ-Comput Inf Sci 31(1):113–124. https://doi.org/10.1016/j.jksuci.2016.12.002

    Article  Google Scholar 

  33. Kumar PM, Devi GU, Basheer S, Parthasarathy P (2020) A study on data de-duplication schemes in cloud storage. Int J Grid Util Comput 11:509–516. https://doi.org/10.1504/IJGUC.2020.108450

    Article  Google Scholar 

  34. Lake BM, Salakhutdinov R, Tenenbaum JB (2015) Human-level concept learning through probabilistic program induction. Science 350:1332–1338. https://doi.org/10.1126/science.aab3050

    Article  MathSciNet  MATH  Google Scholar 

  35. Latif A, Rasheed A, Sajid U, Ahmed J, Ali N, Ratyal NI, Zafar B, Dar SH, Sajid M, Khalil T (2019) Content-based image retrieval and feature extraction: a comprehensive review. Math Probl Eng. https://doi.org/10.1155/2019/9658350

  36. Lee SH, Chan CS, Wilkin P, Remagnino P (2015) Deep-plant: plant identification with convolutional neural networks. Proceedings - International Conference on Image Processing, ICIP, pp.452–456. https://doi.org/10.1109/ICIP.2015.7350839

  37. Lei Y, Qiu G, Zheng L, Huang J (2014) Fast near-duplicate image detection using uniform randomized trees. ACM Trans Multimed Comput Commun Appl (TOMM) 10(4):1–15. https://doi.org/10.1145/2602186

    Article  Google Scholar 

  38. Leutenegger S, Chli M, Siegwart RY (2011) BRISK: binary robust invariant scalable keypoints. Proceedings of the IEEE International Conference on Computer Vision, 2548–2555. https://doi.org/10.1109/ICCV.2011.6126542

  39. Li L, Zic J (2014) Image matching algorithm based on feature-point and DAISY descriptor. J Multimed 9(6):829–834. https://doi.org/10.4304/jmm.9.6.829-834

    Article  Google Scholar 

  40. Li J, Qian X, Li Q, Zhao Y, Wang L, Tang YY (2015) Mining near duplicate image groups. Multimed Tools Appl 74(2):655–669. https://doi.org/10.1007/s11042-014-2008-0

    Article  Google Scholar 

  41. Li X, Li J, Huang F (2016) A secure cloud storage system supporting privacy-preserving fuzzy deduplication. J Soft Comput 20(4):1437–1448. https://doi.org/10.1007/s00500-015-1596-6

    Article  Google Scholar 

  42. Liang S, Wang P (2020) An efficient hierarchical near-duplicate video detection algorithm based on deep semantic features. In: Ro Y et al (eds) MultiMedia modeling. MMM lecture notes in computer science, vol 11961. Springer. https://doi.org/10.1007/978-3-030-37731-1_61

    Chapter  Google Scholar 

  43. Liu YH (2018) Feature extraction and image recognition with convolutional neural networks. J Phys Conf Ser 1087(6):062032

    Article  Google Scholar 

  44. Liu L, Lu Y, Suen CY (2015) Variable-length signature for near-duplicate image matching. IEEE Trans Image Process 24(4):1282–1296. https://doi.org/10.1109/TIP.2015.2400229

    Article  MathSciNet  MATH  Google Scholar 

  45. Liu D, Shen J, Wang A, Wang C (2020) Secure real-time image protection scheme with near-duplicate detection in cloud computing. J Real-Time Image Proc 17:175–184. https://doi.org/10.1007/s11554-019-00887-6

    Article  Google Scholar 

  46. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94

    Article  Google Scholar 

  47. Maji S, Rahtu E, Kannala J, Blaschko M, Vedaldi A (2013) Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151

    Google Scholar 

  48. Mao B, Jiang H, Wu S, Fu Y, Tian L (2014) Read-performance optimization for deduplication-based storage Systems in the Cloud. ACM Trans Storage 10(2):1–22. https://doi.org/10.1145/2512348

    Article  Google Scholar 

  49. Miksik O, Mikolajczyk K (2012) Evaluation of local detectors and descriptors for fast feature matching. In: Proceedings of 21st international conference of pattern recognition (ICPR), 2012, pp. 2681-2684. IEEE

  50. Mohamed SMA, Wang Y (2020) A survey on novel classification of deduplication storage systems. Distrib Parallel Databases. https://doi.org/10.1007/s10619-020-07301-2

  51. Mohapatra S, Bajpai N, Swarnkar T, Mishra M (2020) Raw data redundancy elimination on cloud database. In: Das A, Nayak J, Naik B, Dutta S, Pelusi D(eds), Computational intelligence in pattern recognition. Adv Intel Sys Comput 1120:395–405. https://doi.org/10.1007/978-981-15-2449-3_34

    Article  Google Scholar 

  52. Nbt Y, Ismail A, Majid NAA (2016) Deduplication image middleware detection comparison in standalone cloud database. Int J Adv Comput Sci Technol (IJACST) 5:12–18

    Google Scholar 

  53. Nian F, Li T, Wu X, Gao Q, Li F (2016) Efficient near-duplicate image detection with a local-based binary representation. Multimed Tools Appl 75(5):2435–2452. https://doi.org/10.1007/s11042-015-2472-1

    Article  Google Scholar 

  54. Nilsback ME, Zisserman A (2006) A visual vocabulary for flower classification. In: IEEE computer society conference on computer vision and Pattern Recognition (CVPR'06), 2:1447-1454. https://doi.org/10.1109/CVPR.2006.42

  55. Pang Y, Li W, Yuan Y, Pan J (2012) Fully affine invariant SURF for image matching. Neurocomputing 85:6–10. https://doi.org/10.1016/j.neucom.2011.12.006

    Article  Google Scholar 

  56. Paulin M, Douze M, Harchaoui Z, Mairal J, Perronin F, Schmid C (2015) Local convolutional features with unsupervised training for image retrieval. In: Proceedings of the IEEE international conference on computer vision, pp. 91-99

  57. Paulo J, Pereira J (2014) A survey and classification of storage deduplication systems. ACM Comput Surv 47(1):1–30. https://doi.org/10.1145/2611778

    Article  Google Scholar 

  58. Peker KA (2011) Binary sift: fast image retrieval using binary quantized sift features. In: Content-based multimedia indexing (CBMI), in 9th international workshop on 2011, pp. 217-222, IEEE. https://doi.org/10.1109/CBMI.2011.5972548

  59. Pietro RD, Sorniotti A (2016) Proof of ownership for deduplication systems: a secure, scalable, and efficient solution. Comput Commun 82:71–82. https://doi.org/10.1016/j.comcom.2016.01.011

    Article  Google Scholar 

  60. Raghavendra U, Fujita H, Bhandary SV, Gudigar A, Hong TJ, Acharya UR (2018) Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Inf Sci 441:41–49. https://doi.org/10.1016/j.ins.2018.01.051

    Article  MathSciNet  Google Scholar 

  61. Ramaiah NP, Mohan CK (2011) De-duplication of photograph images using histogram refinement. IEEE Recent Advances in Intelligent Computational Systems 391–395. https://doi.org/10.1109/RAICS.2011.6069341

  62. Seo JS, Haitsma J, Kalker T, Yoo CD (2004) A robust image fingerprinting system using the radon transform. Signal Process Image Commun 19(4):325–339. https://doi.org/10.1016/j.image.2003.12.001

    Article  Google Scholar 

  63. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

    Google Scholar 

  64. Song J (2017) Binary generative adversarial networks for image retrieval. arXiv preprint arXiv:1708.04150

    Google Scholar 

  65. Song J, Yang Y, Huang Z, Shen HT, Luo J (2013) Effective multiple feature hashing for large-scale near-duplicate video retrieval. IEEE Trans Multimedia 15:1997–2008. https://doi.org/10.1109/TMM.2013.2271746

    Article  Google Scholar 

  66. Song J, Zhang H, Li X, Gao L, Wang M, Hong R (2018) Self-supervised video hashing with hierarchical binary auto-encoder. IEEE Trans Image Process 27:3210–3221. https://doi.org/10.1109/TIP.2018.2814344

    Article  MathSciNet  MATH  Google Scholar 

  67. Song J, He T, Gao L, Xu X, Hanjalic A, Shen HT (2020) Unified binary generative adversarial network for image retrieval and compression. Int J Comput Vis 128:2243–2264. https://doi.org/10.1007/s11263-020-01305-2

    Article  MathSciNet  MATH  Google Scholar 

  68. Srinivas S, Sarvadevabhatla RK, Mopuri KR, Prabhu N, Kruthiventi SS, Babu RV (2016) A taxonomy of deep convolutional neural nets for computer vision. Front Robot AI https://doi.org/10.3389/frobt.2015.00036

  69. Sujatha G, Raj JR (2020) A study on image hashing techniques for implementing deduplication. In: Hemanth D., Vadivu G., Sangeetha M., Balas V. (eds), In Artificial Intelligence Techniques for Advanced Computing Applications. Lecture Notes in Networks and Systems 130:529-534. https://doi.org/10.1007/978-981-15-5329-5_49

  70. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, https://doi.org/10.1109/CVPR.2015.7298594

  71. Takeshita J, Karl R, Jung T (2020) Secure single-server nearly-identical image deduplication. arXiv preprint arXiv:2005.02330

    Book  Google Scholar 

  72. Thaiyalnayaki S, Sasikala J, Ponraj R (2018) Indexing near-duplicate images in web search using minhash algorithm. Mater Today: Proc 5(1):1943–1949. https://doi.org/10.1016/j.matpr.2017.11.297

    Article  Google Scholar 

  73. Thaiyalnayaki S, Sasikala J, Ponraj R (2019) Detecting near-duplicate images using segmented minhash algorithm. Int J Adv Intell Paradigms 12:192–206. https://doi.org/10.1504/IJAIP.2019.096963

    Article  Google Scholar 

  74. Thyagharajan KK, Kalaiarasi G (2020) A review on near-duplicate detection of images using computer vision techniques. Arch Comput Methods Eng 6:1–20. https://doi.org/10.1007/s11831-020-09400-w

    Article  Google Scholar 

  75. Tian Y, Luo P, Wang X, Tang X (2015) Deep learning strong parts for pedestrian detection. Proceedings of the IEEE International Conference on Computer Vision, 2015 Inter, 1904–1912. https://doi.org/10.1109/ICCV.2015.221

  76. Vedaldi A, Lenc K (2015) MatConvNet - Convolutional Neural Networks for MATLAB. In: Proceedings of the 23rd ACM international conference on Multimedia, 2015 pp. 689-692. https://doi.org/10.1145/2733373.2807412

  77. Velmurugan K, Baboo LDSS (2011) Content-based image retrieval using SURF and colour moments. Global J Comput Sci Technol 11(10)

  78. Wan J, Han S, Zhang J, Zhu B, Zhou L (2013) An image management system implemented on open-source cloud platform. Proceedings - IEEE 27th International Parallel and Distributed Processing Symposium Workshops and PhD Forum, IPDPSW 2064–2070. https://doi.org/10.1109/IPDPSW.2013.176

  79. Wang J, Chen X (2016) Efficient and secure storage for outsourced data: a survey. Data Sci Eng 1(3):178–188. https://doi.org/10.1007/s41019-016-0018-9

    Article  Google Scholar 

  80. Wang JG, Li J, Lee CY, Yau WY (2010) Dense SIFT and Gabor descriptors-based face representation with applications to gender recognition. In: 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, 1860–1864. https://doi.org/10.1109/ICARCV.2010.5707370

  81. Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3D shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 1912–1920

  82. Xia W, Zou X, Jiang H, Zhou Y, Liu C, Feng D, Hua Y, Hu Y, Zhang Y (2020) The Design of Fast Content-Defined Chunking for data deduplication based storage systems. IEEE Trans Parallel Distrib Syst 31:2017–2031. https://doi.org/10.1109/TPDS.2020.2984632

    Article  Google Scholar 

  83. Yang S, Luo P, Loy CC, Tang X (2015) From facial parts responses to face detection: a deep learning approach. In: Proceedings of the IEEE International Conference on Computer Vision 2015, pp. 3676-3684. https://doi.org/10.1109/ICCV.2015.419

  84. Yao J, Yang B, Zhu Q (2015) Near-duplicate image retrieval based on contextual descriptor. IEEE Signal Process Lett 22(9):1404–1408 https://digitalcommons.unomaha.edu/compscifacpub/25

    Article  Google Scholar 

  85. Yu X, Huang T (2009) A SIFT-based image fingerprinting approach robust to geometric transformations. In: Circuits and systems, ISCAS 2009. IEEE international symposium on, pp. 1665-1668. IEEE. https://doi.org/10.1109/ISCAS.2009.5118093

  86. Zargar AJ, Singh N, Rathee G, Singh AK (2015) Image data-deduplication using the block truncation coding technique. IEEE International Conference on Futuristic Trends in Computational Analysis and Knowledge Management 154–158. https://doi.org/10.1109/ABLAZE.2015.7154986

  87. Zeng X, Wen S, Zeng Z, Huang T (2018) Design of memristor-based image convolution calculation in convolutional neural network. Neural Comput & Applic 30(2):503–508. https://doi.org/10.1007/s00521-016-2700-2

    Article  Google Scholar 

  88. Zhang J, Feng Z, Su Y (2008) A new approach for detecting copy-move forgery in digital images. 11th IEEE Singapore International Conference on Communication Systems, ICCS 2008, 362–366. https://doi.org/10.1109/ICCS.2008.4737205

  89. Zhang D, Sun Z, Jia K (2020) Near-duplicate video detection based on temporal and spatial key points. In: Kountchev R, Patnaik S, Shi J, Favorskaya M (eds) Advances in 3D image and graphics representation, analysis, computing and information technology. Smart innovation, systems and Technologies, vol 180. Springer, Singapore, pp 129–137. https://doi.org/10.1007/978-981-15-3867-4_16

    Chapter  Google Scholar 

  90. Zhao WL, Ngo CW, Tan HK, Wu X (2007) Near-duplicate keyframe identification with interest point matching and pattern learning. IEEE Trans Multimedia 9(5):1037–1048. https://doi.org/10.1109/TMM.2007.898928

    Article  Google Scholar 

  91. Zhou W, Newsam S, Li C, Shao Z (2018) PatternNet: a benchmark dataset for performance evaluation of remote sensing image retrieval. ISPRS J Photogramm Remote Sens 145:197–209. https://doi.org/10.1016/j.isprsjprs.2018.01.004

    Article  Google Scholar 

  92. Zuo F, de With PH (2004) Fast facial feature extraction using a deformable shape model with haar-wavelet based local texture attributes. In: IEEE international conference on image processing, ICIP'04, 3:1425-1428. https://doi.org/10.1109/ICIP.2004.1421330

  93. Zuo P, Hua Y, Liu X, Feng D, Xia W, Cao S, Wu J, Sun Y, Guo Y (2017) BEES: bandwidth- and energy- efficient image sharing for real-time situation awareness. Proceedings - International Conference on Distributed Computing Systems, 1510–1520. https://doi.org/10.1109/ICDCS.2017.36

Download references

Acknowledgements

This research is supported by the Department of Science and Technology, Government of India under WOS (Women Scientists Scheme) sponsored research project entitled “Distributed Data Deduplication Technique for efficient Cloud-Based Storage System” under File No: SR/WOS-A/ET-119/2016.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology (Deemed to be University), Patiala, Punjab, India

    Ravneet Kaur, Jhilik Bhattacharya & Inderveer Chana

Authors
  1. Ravneet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jhilik Bhattacharya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Inderveer Chana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ravneet Kaur.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interests.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, R., Bhattacharya, J. & Chana, I. Deep CNN based online image deduplication technique for cloud storage system. Multimed Tools Appl 81, 40793–40826 (2022). https://doi.org/10.1007/s11042-022-13182-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13182-7

Keywords

Search

Navigation