Constraint and Descriptor Based Image Retrieval through Sketches with Data Retrieval using Reversible Data Hiding

Authors

  • Dipika Birari 1 Research Scholar, Faculty of Computer Engineering, Pacific Academy of Higher Education and Research University, Udaipur, Rajasthan, India https://orcid.org/0000-0001-5767-6157
  • Dilendra Hiran Pacific Institute of Computer Applications, Udaipur, Rajasthan, India
  • Vaibhav Narawade Department of Computer Engineering Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, Maharashtra, India

Keywords:

Image retrieval, Descriptor, Data retrieval, Edge extraction, Sketch-based, Grayscale, Invariance, feature extraction.

Abstract

An image retrieval system includes image retrieval through sketches. Sketches act as an outline for any object with few details. "Sketch-Based Image Retrieval (SBIR)" is universally recognized as an extension of image retrieval by such rough sketching that concentrates on the main features of the object.  SBIR has become an effective and popular image mining search technique as the demand for multimedia technology has grown. Due to the less precise depiction in sketches, comparing such sketches to real colorful and meaningful images becomes extremely difficult. As a solution to the captioned matter, the proposed approach incorporates Histogram Line Relationship (HLR) descriptors to facilitate constraint-based image retrieval. After pre-processing, the descriptor describes the visual features of an image. Here edge length-based constraints make SBIR powerful enough to select strong shaping edges. This approach is further enhanced to include data retrieval and is referred to as "Sketch-Based Image and Data Retrieval (SBIDR)" which even makes it more functional. Throughout image processing, the data embedding and extraction procedure are carried out using the Reversible Data Hiding (RDH) technique with an invariant grayscale version. The proposed method employs a hybrid model of image retrieval and data retrieval system with the addition of constraints and grayscale invariance. This models produce efficient outcomes in terms of retrieval.

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References

Shu Wang, Jian Zhang, Tony X. Han and Zhenjiang Miao, "Sketch Based Image Retrieval Through Hypothesis-Driven Object Boundary Selection With HLR Descriptor" , IEEE transactions on Multimedia, Vol. 17, No. 7 July 2015.

Dongdong Hou, Weiming Zhang, Kejiang Chen, Sian-Jheng Lin and Nenghai Yu, " Reversible Data Hiding in Color Image with Grayscale Invariance." , in Transaction on circuits and Systems for Video Technology. 1051-8215, 2018.

Kede Ma, Weiming Zhang, Xianfeng Zhao, Nenghai Yu, and Fenghua Li, “Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption”, IEEE Transactions on Information Forensics and Security, Vol. 8, No. 3, March 2013.

J. Canny, ‘‘A computational approach to edge detection,’’ IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-8, no. 6, pp. 679–698, Nov. 1986.

M. Eitz, K. Hildebrand, T. Boubekeur, and M. Alexa, “A descriptor for large scale image retrieval based on sketched feature lines,”,in Proc. 6th Eurograph. Symp. Sketch-Based Interfaces Modeling, 2009, pp. 2936.

R. Hu, M. Barnard, and J. Collomosse, ‘‘Gradient field descriptor for sketch based retrieval and localization,’’ in Proc. IEEE Int. Conf. Image Process. (ICIP), Sep. 2010, pp. 1025–1028.

M. Eitz, K. Hildebrand, T. Boubekeur, and M. Alexa, ‘‘Sketch-based image retrieval: Benchmark and bag-of-features descriptors,’’ IEEE Trans. Vis. Comput. Graphics, vol. 17, no. 11, pp. 1624–1636, Nov. 2011.

Y. Cao, C. Wang, L. Zhang and L.Zhang, “Edgel index for large –scale Sketch-bassed image search,” in Proc. IEEE conf. omput. Vis Pattern Recognit.(CVPR), Jun. 2011, pp. 761-768.

K. Bozas and E. Izquierdo, “Large scale sketch based image retrieval using patch hashing,” in Adv. Visual Comput., vol. 7431, pp. 210219, 2012.

R. Hu and J. Collomosse, “A performance evaluation of gradient field hog descriptor for sketch based image retrieval.” in Comput. Vis. Image Understand., vol. 117, no. 7, pp. 790806, 2013.

C. Yang, O. Tiebe, P. Pietsch, C. Feinen, U. Kelter, and M. Grzegorzek,‘‘Shape-based object retrieval by contour segment matching,’’ in Proc. IEEE Int. Conf. Image Process. (ICIP), Oct. 2014, pp. 2202–2206.

Tingting Liu, Haiyong Xu,“Medical Image Segmentation Based on a Hybrid Region-Based Active Contour Model”, Research Article, Comput. Math Methods in Med., 2014.

Y H Sharath Kumara, D S Gurub, “Retrieval of Flower Based on Sketches,” in International Conference on Information and Communication Technologies, Procedia Computer Science 46 (2015 ) 1577 – 1584.

C. Xiao, C. Wang, L. Zhang, and L. Zhang, ‘‘Sketch-based image retrieval via shape words,’’ in Proc. ACM Int. Conf. Multimedia Retr. (ICMR), 2015,pp. 571–574.

Jingyu Wang, Yu Zhao , Qi Qi, Qiming Huo, Jian Zou, Ce Ge, And Jianxin Liao, “MindCamera: Interactive Sketch-Based Image Retrieval and Synthesis.” , in Special Section On Recent Advantages Of Computer Vision Based On Chinese Conference On Computer Vision (CCCV) 2017, vol 6, 2018.

T. Chen, M.-M. Cheng, P. Tan, A. Shamir, and S.-M. Hu, ``Sketch2Photo: Internet image montage,'' ACM Trans. Graph., vol. 28, no. 5, Dec. 2009,Art. no. 124.

M. Eitz, R. Richter, K. Hildebrand, T. Boubekeur, and M. Alexa,``Photosketcher: Interactive sketch-based image synthesis,'' IEEEComput. Graph. Appl., vol. 31, no. 6, pp. 56-66, Nov./Dec. 2011.

P. Arbeláez, M. Maire, C. Fowlkes, and J. Malik, ``Contour detection and hierarchical image segmentation,'' IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 898-916, May 2011.

T. Bui and J. Collomosse, ``Scalable sketch-based image retrieval using color gradient features,'' in Proc. IEEE Int. Conf. Comput. Vis. (ICCV)Workshop, Dec. 2015, pp. 1012-1019.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, ``You only look once:Unied, real-time object detection,'' in Proc. IEEE Conf. Comput. Vis.Pattern Recognit. (CVPR), Jun. 2016, pp. 779-788.

P. Xu et al., ``Cross-modal subspace learning for ne-grained sketch-based image retrieval,'' Neurocomputing, vol. 278, pp. 7586, Feb. 2018, doi: 10.1016/j.neucom.2017.05.099.

M.-M. Cheng, N. J. Mitra, X. Huang, P. H. S. Torr, and S.-M. Hu, ``Global contrast based salient region detection,'' IEEE Trans. Pattern Anal. Mach. Intell., vol. 37, no. 3, pp. 569582, Mar. 2015.

S. Parui and A. Mittal, “Similarity-invariant sketch-based image retrieval in large databases,” in Proc. 13th Eur. Conf. Comput. Vis. Conf. Comput. Vis., 2014, vol. 8694, pp. 398–414.[31]

C. Ma, X. Yang, C. Zhang, X. Ruan, M.-H. Yang, and O. Coporation, “Sketch retrieval via dense stroke features,” in Proc. Brit. Mach. Vis.Conf., 2013, vol. 2, pp. 65.1–65.11. [9]

X. Cao, H. Zhang, S. Liu, X. Guo, and L. Lin, “Sym-fish: A symmetry aware flip invariant sketch histogram shape descriptor,” in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2013, pp. 313–320.

D. Birari, D. Hiran, V. Narawade, “Sketch Based Data Retrieval using Reversible Data Hiding on Images”, in JUSST vol 22, ISSN 1007- 6735, Dec 2020.

D. Birari, D. Hiran, V. Narawade “Image Retrieval through sketches based on Descriptor with Data Retrieval using Reversibility Method” in Proc. IEEE Int. Conf. for Advancement in Technology, Jan 2022.

J. Fridrich and M. Goljan, “Lossless Data Embedding for All Image Formats,” in SPIE Proceedings of Photonics West, Electronic Imaging, Security and Watermarking of Multimedia Contents, vol. 4675, pp. 572- 583, San Jose, Jan. 2002.

J. Tian, “Reversible Data Embedding Using a Difference Expansion,” IEEE Trans. Circuits System and Video Technology, vol. 13, no. 8, pp. 890-896, Aug. 2003.

M. Kutter and S. Winkler, “A vision-based masking model for spread- spectrum image watermarking,” IEEE Trans. Image Processing, vol. 11, no. 1, pp. 16-25, Jan. 2002.

P. Bao and X. Ma, “Image adaptive watermarking using wavelet domain singular value decomposition,” IEEE Trans. Circuits and Systems for Video Technology, vol. 15, no. 1, pp. 96-102, Jan. 2005.

M. Asikuzzaman, M. J. Alam, A. J. Lambert, and M. R. Pickering, “Imperceptible and robust blind video watermarking using chrominance embedding: A set of approaches in the DT CWT domain,” IEEE Trans. Information Forensics and Security, vol. 9, no. 9, pp. 1502-1517, Sep. 2014

M. R. A.Lari, S. Ghofrani, and D. McLernon, “Using curvelet transform for watermarking based on amplitude modulation,” Signal, Image and Video Processing, vol. 8, no. 4, pp. 687-697, May 2014.

C. H. Chou and K. C. Liu, “A perceptually tuned watermarking scheme for color images,” IEEE Trans. Image Processing, vol. 19, no. 11, pp. 2966-2982, Nov. 2010.

Y. He, W. Liang, J. Liang, and M. Pei, “Tensor decomposition based color image watermarking,” Proceedings of SPIE, vol. 9069, pp. 90690U- 90690U-6, Jan. 2014.

C. Chang, C. Lin, Y. Fan, “Lossless data hiding for color images based on block truncation coding,” Pattern Recognition, vol. 41, no. 7, pp. 2347-2357, 2008.

J. Li, X. Li, and B. Yang, “Reversible data hiding scheme for color image based on prediction-error expansion and cross-channel correlation,” Signal Processing, vol. 93, no. 9, pp. 2748-2758, 2013.

B. Ou, X. Li, Y. Zhao, and R. Ni, “Efficient color image reversible data hiding based on channel-dependent payload partition and adaptive embedding,” Signal Processing, vol. 108, pp. 642-657, 2015.

H. Bay, T. Tuytelaars, and L. J. V. Gool, SURF: Speeded up robust features, in Proc. Eur. Conf. Comput. Vis., pp. 404-417, 2008.

Sowmya V, Govind D, Soman K P, “Significance of incorporating chrominance information for effective color-to-grayscale image conversion,” Signal, Image and Video Processing, vol. 11, pp. 129-136, 2017.

D. Birari, D. Hiran, V. Narawade, “A Survey on Sketch Based Image and Data Retrieval.” In Proc. Springer Int. Conf. on Communication and Cyber Physical Engg. (ICCCE), vol 570, pp. 285-290, 2019.

D. P. Gadekar, S N Popat, A. H. Raut, “Exploring Data Security Scheme into Cloud Using Encryption Algorithms” International Journal of Recent Technology and Engineering , Volume-8 Issue-2, July2019,

S N Popat, Y. P. Singh,” Efficient Research on the Relationship Standard Mining Calculations in Data Mining” in Journal of Advances in Science and Technology | Science & Technology, Vol. 14, Issue No. 2, September-2017,

S N Popat*, Y. P. Singh,” Analysis and Study on the Classifier Based Data Mining Methods” in Journal of Advances in Science and Technology Vol. 14, Issue No. 2, September-2017.

Image and Data Retrieval through Sketches

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Published

19.10.2022

How to Cite

[1]
D. . Birari, D. . Hiran, and V. . Narawade, “Constraint and Descriptor Based Image Retrieval through Sketches with Data Retrieval using Reversible Data Hiding”, Int J Intell Syst Appl Eng, vol. 10, no. 1s, pp. 409–417, Oct. 2022.