Abstract
A content-based image Retrieval (CBIR) has become an essential tool for managing and searching large-scale images. However, the accuracy and performance of CBIR systems can be improved by combining data mining techniques. Content-based retrieval (CBR) uses the properties and characteristics of the content itself to search for and retrieve information from a big database instead of depending on text or metadata. CBR is very helpful in research, where it is necessary to swiftly and effectively examine vast amounts of data. According to the results, data mining techniques can considerably increase the retrieval process accuracy and effectiveness. Similar photos can be grouped together using clustering algorithms, common patterns of visual features can be found using association rule mining, and images can be classified using classification techniques. In order to create a system for content-based image retrieval and processing, we studied the retrieval of images from huge databases using a variety of feature extraction and matching techniques. The demand for CBIR development came as a result of the sharp rise in image database volumes and their widespread use in several applications. The description of basic feature extraction methods including texture, color, and form is provided in this study. Once these features are retrieved and then used for comparing photos based on similarity. This study suggests a cutting-edge system design for CBIR system that integrates content-based picture and color analysis with data mining methods. This work is intended to develop a segmentation module for the CBIR system.
References
Shu X, Ye Y (2023) Knowledge discovery: methods from data mining and machine learning. Soc Sci Res 110:102817
Ajam A, Forghani M, AlyanNezhadi MM, Qazanfari H, Amiri Z (2019) Content-based image retrieval using color difference histogram in image textures. In: 2019 5th Iranian conference on signal processing and intelligent systems (ICSPIS). IEEE, pp 1–6
Kayhan N, Fekri-Ershad S (2021) Content based image retrieval based on weighted fusion of texture and color features derived from modified local binary patterns and local neighborhood difference patterns. Multimed Tools Appl 80(21–23):32763–32790
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
Alsmadi MK (2020) Content-based image retrieval using color, shape and texture descriptors and features. Arab J Sci Eng 45(4):3317–3330
Ahmad F, Ahmad T (2021) Content based image retrieval system based on deep convolution neural network model by integrating three-fold geometric augmentation. Optical Memory Neural Netw 30:236–249
Dubey SR (2021) A decade survey of content based image retrieval using deep learning. IEEE Trans Circuits Syst Video Technol 32(5):2687–2704
Ahmed KT, Ummesafi S, Iqbal A (2019) Content based image retrieval using image features information fusion. Inf Fusion 51:76–99
Singh S, Batra S (2020) An efficient bi-layer content based image retrieval system. Multimed Tools Appl 79(25–26):17731–17759
Salih SF, Abdulla AA (2023) An effective bi-layer content-based image retrieval technique. J Supercomput 79(2):2308–2331
Garg M, Dhiman G (2021) A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants. Neural Comput Appl 33:1311–1328
Mengash HA (2020) Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access 8:55462–55470
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kaur, J., Gupta, D., Singh, A., Shah, S.H.A. (2024). Leveraging Content Based Image Retrieval Using Data Mining for Efficient Image Exploration. In: Marriwala, N.K., Dhingra, S., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. MRCN 2023. Lecture Notes in Networks and Systems, vol 915. Springer, Singapore. https://doi.org/10.1007/978-981-97-0700-3_14
Download citation
DOI: https://doi.org/10.1007/978-981-97-0700-3_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-0699-0
Online ISBN: 978-981-97-0700-3
eBook Packages: EngineeringEngineering (R0)