Abstract
Content-based image retrieval (CBIR) systems find a lively application in various fields like medical diagnosis, crime prevention, art collection, textile industry, etc. CBIRs continue to be an active domain of research due to increasing image databases and complex user queries being generated. The major challenge faced by such systems is the existence of a semantic gap between low-level features and human perception of the object’s images. Many CBIR systems exist in literature which aims to reduce this gap so as to produce precise search results for complex user queries. So this article aims to analyze and survey such systems based on the preprocessing, feature extraction, and classification techniques they employ for image retrieval while simultaneously focussing on the associated advantages and disadvantages for each of the discussed schemes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Long F, Zhang H, Feng DD (2003) Fundamentals of content-based image retrieval. In: Multimedia information retrieval and management. Springer, Berlin, Heidelberg, 1–26
Gudivada VN, Raghavan VV (1995) Content based image retrieval systems. Computer 28(9):18–22
Kim W-Y, Kim Y-S (2000) A region-based shape descriptor using Zernike moments. Sig Process Image Commun 16(1-2):95–102
Kumar G, Pradeep KB (2014) A detailed review of feature extraction in image processing systems. In: 2014 Fourth international conference on advanced computing & communication technologies. IEEE
Zhang H-J, Zhong S, Zhu X (2006) Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR). U.S. Patent No. 7,113,944. 26 Sep. 2006
Mistry Y, Ingole DT, Ingole MD (2018). Content based image retrieval using hybrid features and various distance metric. J Electri Syst Inf Technol 5(3):874–888
Meshram SP, Thakare AD, Gudadhe S (2016) Hybrid swarm intelligence method for post clustering content based image retrieval. Procedia Comput Sci 79:509–515
Bala A, Kaur T (2016) Local texton XOR patterns: a new feature descriptor for content-based image retrieval. Eng Sci Technol Int J 19(1):101–112
Sutojo T et al (2017) CBIR for classification of cow types using GLCM and color features extraction. In: 2017 2nd international conferences on information technology, information systems and electrical engineering (ICITISEE). IEEE
Atlam HF, Attiya G, El-Fishawy N (2017) Integration of color and texture features in CBIR system. Int J Comput Appl 164(3):23–29
Bhadoria S et al (2012) Comparison of Color, Texture and ICM Features in CBIR System. In: Advanced materials research, vol. 403. Trans Tech Publications Ltd
David HBF, Balasubramanian R, Pandian AA (2018) CBIR using multi-resolution transform for brain tumour detection and stages identification. Int J Biomed Biolog Eng 10(11):543–553
Rahmani MKI, Ansari MA, Goel AK (2015) An efficient indexing algorithm for CBIR. In: 2015 IEEE international conference on computational intelligence & communication technology. IEEE, pp 73–77
Jena B, Nayak GK, Saxena S (2019) Maximum payload for digital image steganography obtained by mixed edge detection mechanism. In: 2019 international conference on information technology (ICIT). IEEE, pp 206–210
Arivazhagan S, Ganesan L, Bama S (2006) Fault segmentation in fabric images using Gabor wavelet transform. Mach Vis Appl 16(6):356
Nason GP, Silverman BW (1995) The stationary wavelet transform and some statistical applications. Wavelets and statistics. Springer, New York, NY, pp 281–299
Kannala J, Rahtu E (2012). Bsif: Binarized statistical image features. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012). IEEE, pp 1363–1366
Chatzichristofis SA, Yiannis SB (2008) CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. In: International conference on computer vision systems. Springer, Berlin, Heidelberg
Ouadfel S, Batouche M, Ahmed-Taleb A (2012) ACPSO: a novel swarm automatic clustering algorithm based image segmentation. In: Multidisciplinary computational intelligence techniques: applications in business, engineering, and medicine. IGI Global, pp 226–238
Cui X, Thomas EP, Paul P (2005) Document clustering using particle swarm optimization. In: Proceedings 2005 IEEE swarm intelligence symposium, 2005. SIS 2005. IEEE
Kanungo T et al (2002) An efficient k-means clustering algorithm: analysis and implementation. In: IEEE Trans Pattern Anal Mach Intell 24(7):881–892
Kao Y, Cheng K (2006) An ACO-based clustering algorithm. International workshop on ant colony optimization and swarm intelligence. Springer, Berlin, Heidelberg
Sebastian V, Unnikrishnan A, Balakrishnan K (2012) Gray level co-occurrence matrices: generalisation and some new features. arXiv preprint arXiv:1205.4831
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jena, B., Nayak, G.K., Saxena, S. (2021). Survey and Analysis of Content-Based Image Retrieval Systems. In: Singh, A.K., Tripathy, M. (eds) Control Applications in Modern Power System. Lecture Notes in Electrical Engineering, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-15-8815-0_37
Download citation
DOI: https://doi.org/10.1007/978-981-15-8815-0_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8814-3
Online ISBN: 978-981-15-8815-0
eBook Packages: EnergyEnergy (R0)