Content-Based Image Retrieval and Feature Extraction: Analysing the Literature
DOI:
https://doi.org/10.36676/jrps.v15.i3.1520Keywords:
Content-based image retrieval (CBIR), image retrieval, imagesAbstract
A significant amount of multimedia data consists of digital images, and multimedia content analysis is used in many real-world computer vision applications. Multimedia information, especially photos, has become much more complicated in the last several years. Every day, millions of photos are posted to various websites, such as Instagram, Facebook, and Twitter. Finding a suitable image in an archive is a difficult research subject for the field of computer vision. Most search engines use standard text-based techniques that depend on metadata and captions in order to fetch photos. Over the past 20 years, a great deal of research has been conducted on content-based image retrieval (CBIR), picture categorization, and analysis. In image classification models and CBIR, high-level picture representations are represented as feature vectors made up of numerical values. Empirical evidence indicates a considerable disparity between picture feature representation and human visual understanding. Reducing the semantic gap between human visual understanding and picture feature representation is the aim of this study. This study aims to do a thorough analysis of the latest advancements in the domains of Content-Based picture Retrieval and picture representation. We performed a comprehensive analysis of many models for image retrieval and picture representation, encompassing the most recent advancements in semantic deep-learning methods and feature extraction. This paper provides an in-depth analysis of the key ideas and important studies related to image representation and content-based picture retrieval. In an effort to stimulate more research in this field, it also offers a preview of potential future study topics.
References
D. Zhang, M. M. Islam, and G. Lu, “A review on automatic image annotation techniques,” Pattern Recognition, vol. 45, no. 1, pp. 346–362, 2012. DOI: https://doi.org/10.1016/j.patcog.2011.05.013
Y. Liu, D. Zhang, G. Lu, and W.-Y. Ma, “A survey of contentbased image retrieval with high-level semantics,” Pattern Recognition, vol. 40, no. 1, pp. 262–282, 2007. DOI: https://doi.org/10.1016/j.patcog.2006.04.045
T. Khalil, M. U. Akram, H. Raja, A. Jameel, and I. Basit, “Detection of glaucoma using cup to disc ratio from spectral domain optical coherence tomography images,” IEEE Access, vol. 6, pp. 4560–4576, 2018. DOI: https://doi.org/10.1109/ACCESS.2018.2791427
S. Yang, L. Li, S. Wang, W. Zhang, Q. Huang, and Q. Tian, “SkeletonNet: a hybrid network with a skeleton-embedding process for multi-view image representation learning,” IEEE Transactions on Multimedia, vol. 1, no. 1, 2019. DOI: https://doi.org/10.1109/TMM.2019.2912735
W. Zhao, L. Yan, and Y. Zhang, “Geometric-constrained multi-view image matching method based on semi-global optimization,” Geo-Spatial Information Science, vol. 21, no. 2, pp. 115–126, 2018. DOI: https://doi.org/10.1080/10095020.2018.1441754
W. Zhou, H. Li, and Q. Tian, “Recent advance in contentbased image retrieval: a literature survey,” 2017, https://arxiv. org/abs/1706.06064.
A. Amelio, “A new axiomatic methodology for the image similarity,” Applied Soft Computing, vol. 81, p. 105474, 2019. DOI: https://doi.org/10.1016/j.asoc.2019.04.043
C. Celik and H. S. Bilge, “Content based image retrieval with sparse representations and local feature descriptors: a comparative study,” Pattern Recognition, vol. 68, pp. 1–13, 2017. DOI: https://doi.org/10.1016/j.patcog.2017.03.006
T. Khalil, M. Usman Akram, S. Khalid, and A. Jameel,“Improved automated detection of glaucoma from fundus image using hybrid structural and textural features,” IET Image Processing, vol. 11, no. 9, pp. 693–700, 2017. DOI: https://doi.org/10.1049/iet-ipr.2016.0812
L. Amelio, R. Jankovi´c, and A. Amelio, “A new dissimilarity measure for clustering with application to dermoscopic images,” in Proceedings of the 2018 9th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–8, IEEE, Zakynthos, Greece, July 2018. DOI: https://doi.org/10.1109/IISA.2018.8633672
S. Susan, P. Agrawal, M. Mittal, and S. Bansal, “New shape descriptor in the context of edge continuity,” CAAI Transactions on Intelligence Technology, vol. 4, no. 2, pp. 101–109, 2019. DOI: https://doi.org/10.1049/trit.2019.0002
L. Piras and G. Giacinto, “Information fusion in content based image retrieval: a comprehensive overview,” Information Fusion, vol. 37, pp. 50–60, 2017. DOI: https://doi.org/10.1016/j.inffus.2017.01.003
L. Amelio and A. Amelio, “Classification methods in image analysis with a special focus on medical analytics machine Learning Paradigms, pp. 31–69, Springer, Basel, Switzerland, 2019. DOI: https://doi.org/10.1007/978-3-319-94030-4_3
D. Ping Tian, “A review on image feature extraction and representation techniques,” International Journal of Multimedia and Ubiquitous Engineering, vol. 8, no. 4, pp. 385–396, 2013.
D. Zhang and G. Lu, “Review of shape representation and description techniques,” Pattern Recognition, vol. 37, no. 1, pp. 1–19, 2004. DOI: https://doi.org/10.1016/j.patcog.2003.07.008
R. Datta, J. Li, and J. Z. Wang, “Content-based image retrieval: approaches and trends of the new age,” in Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 253–262, ACM, Singapore, November 2005. DOI: https://doi.org/10.1145/1101826.1101866
Z. Yu and W. Wang, “Learning DALTS for cross-modal retrieval,” CAAI Transactions on Intelligence Technology, vol. 4, no. 1, pp. 9–16, 2019. DOI: https://doi.org/10.1049/trit.2018.1051
N. Ali, D. A. Mazhar, Z. Iqbal, R. Ashraf, J. Ahmed, and F. Zeeshan, “Content-based image retrieval based on late fusion of binary and local descriptors,” International Journal of Computer Science and Information Security (IJCSIS), vol. 14, no. 11, 2016. DOI: https://doi.org/10.20944/preprints201703.0156.v1
N. Ali, Image Retrieval Using Visual Image Features and Automatic Image Annotation, University of Engineering and Technology, Taxila, Pakistan, 2016.
B. Zafar, R. Ashraf, N. Ali et al., “Intelligent image classification-based on spatial weighted histograms of concentric circles,” Computer Science and Information Systems, vol. 15, no. 3, pp. 615–633, 2018. DOI: https://doi.org/10.2298/CSIS180105025Z
Brar, R., & Billing, S. S. (2016). Certain sufficient conditions for parabolic starlike and uniformly close-to-convex functions. Stud. Univ. Babes-Bolyai Math, 61, 53-62.
U. Markowska-Kaczmar and H. Kwa´snicka, “Deep learning––a new era in bridging the semantic gap,” in Bridging the SemanticGap in Image and Video Analysis, pp. 123–159, Springer, Basel, Switzerland, 2018. DOI: https://doi.org/10.1007/978-3-319-73891-8_7
F. Riaz, S. Jabbar, M. Sajid, M. Ahmad, K. Naseer, and N. Ali, “A collision avoidance scheme for autonomous vehicles inspired by human social norms,” Computers & Electrical Engineering, vol. 69, pp. 690–704, 2018. DOI: https://doi.org/10.1016/j.compeleceng.2018.02.011
H. Shao, Y. Wu, W. Cui, and J. Zhang, “Image retrieval based on MPEG-7 dominant color descriptor,” in Proceedings of the 9th International Conference for Young Computer ScientistsICYCS 2008, pp. 753–757, IEEE, Hunan, China,November 2008. DOI: https://doi.org/10.1109/ICYCS.2008.89
X. Duanmu, “Image retrieval using color moment invariant,” in Proceedings of the 2010 Seventh International Conference on Information Technology: New Generations (ITNG), pp. 200–203, IEEE, Las Vegas, NV, USA, April 2010. DOI: https://doi.org/10.1109/ITNG.2010.231
X.-Y. Wang, B.-B. Zhang, and H.-Y. Yang, “Content-based image retrieval by integrating color and texture features,” Multimedia Tools and Applications, vol. 68, no. 3, pp. 545–569, 2014. DOI: https://doi.org/10.1007/s11042-012-1055-7
H. Zhang, Z. Dong, and H. Shu, “Object recognition by a complete set of pseudo-Zernike moment invariants,” in Proceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 930–933, IEEE, Dallas, TX, USA, March 2010. DOI: https://doi.org/10.1109/ICASSP.2010.5495286
Brar, T. P. S. (2016). Green banking adoption: A comparative study of Indian public and private sector banks. Envision Journal of Commerce Department of ACFA, 10.
Y. Liu, D. Zhang, and G. Lu, “Region-based image retrieval with high-level semantics using decision tree learning,”Pattern Recognition, vol. 41, no. 8, pp. 2554–2570, 2008. DOI: https://doi.org/10.1016/j.patcog.2007.12.003
M. M. Islam, D. Zhang, and G. Lu, “Automatic categorization of image regions using dominant color based vector quantization,” in Proceedings of the Digital Image Computing: Techniques and Applications, pp. 191–198, IEEE, Canberra, Australia, December 2008. DOI: https://doi.org/10.1109/DICTA.2008.17
Z. Jiexian, L. Xiupeng, and F. Yu, “Multiscale distance coherence vector algorithm for content-based image retrieval,” @e Scientific World Journal, vol. 2014, Article ID 615973, 13 pages, 2014. DOI: https://doi.org/10.1155/2014/615973
G. Papakostas, D. Koulouriotis, and V. Tourassis, “Feature extraction based on wavelet moments and moment invariants in machine vision systems,” in Human-Centric Machine Vision, InTech, London, UK, 2012. DOI: https://doi.org/10.5772/33141
G.-H. Liu, Z.-Y. Li, L. Zhang, and Y. Xu, “Image retrieval based on micro-structure descriptor,” Pattern Recognition, vol. 44, no. 9, pp. 2123–2133, 2011. DOI: https://doi.org/10.1016/j.patcog.2011.02.003
X.-Y. Wang, Z.-F. Chen, and J.-J. Yun, “An effective method for color image retrieval based on texture,” Computer Standards & Interfaces, vol. 34, no. 1, pp. 31–35, 2012. DOI: https://doi.org/10.1016/j.csi.2011.05.001
R. Ashraf, K. Bashir, A. Irtaza, and M. Mahmood, “Content based image retrieval using embedded neural networks with bandletized regions,” Entropy, vol. 17, no. 6, pp. 3552–3580,2015. DOI: https://doi.org/10.3390/e17063552
A. Irtaza and M. A. Jaffar, “Categorical image retrieval through genetically optimized support vector machines (GOSVM) and hybrid texture features,” Signal, Image and Video Processing, vol. 9, no. 7, pp. 1503–1519, 2015. DOI: https://doi.org/10.1007/s11760-013-0601-8
C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 2, no. 3, pp. 1–27, 2011. DOI: https://doi.org/10.1145/1961189.1961199
S. Fadaei, R. Amirfattahi, and M. R. Ahmadzadeh, “Local derivative radial patterns: a new texture descriptor for content-based image retrieval,” Signal Processing, vol. 137,pp. 274–286, 2017. DOI: https://doi.org/10.1016/j.sigpro.2017.02.013
X. Wang and Z. Wang, “A novel method for image retrieva l based on structure elements’ descriptor,” Journal of Visual Communication and Image Representation, vol. 24, no. 1, pp. 63–74, 2013. DOI: https://doi.org/10.1016/j.jvcir.2012.10.003
Brar, R., & Billing, S. S. (2018). On Certain Results Involving a Multiplier Transformation in a Parabolic Region. Khayyam Journal of Mathematics, 4(2), 123-143.
J. M. Guo, H. Prasetyo, and J. H. Chen, “Content-based image retrieval using error diffusion block truncation coding features,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 3, pp. 466–481, 2015. DOI: https://doi.org/10.1109/TCSVT.2014.2358011
Brar, T. P. S. (2021). Digital marketing performance: Understanding the challenges and measuring the outcomes. In Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing (pp. 51-63). IGI Global. DOI: https://doi.org/10.4018/978-1-7998-7231-3.ch004
G. Qi, H. Wang, M. Haner, C. Weng, S. Chen, and Z. Zhu,“Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation,” CAAITransactions on Intelligence Technology, vol. 4, no. 2, pp. 80–91, 2019. DOI: https://doi.org/10.1049/trit.2018.1045
Kumar, Y., Brar, T. P. S., Kaur, C., & Singh, C. (2024). A Comprehensive Study of Deep Learning Methods for Kidney Tumor, Cyst, and Stone Diagnostics and Detection Using CT Images. Archives of Computational Methods in Engineering, 1-26. DOI: https://doi.org/10.1007/s11831-024-10112-8
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