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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES)
Feroza D. Mirajkar
Khaja Banda Nawaz College of Engineering (KBNCE)
Economics, Econometrics & Finance

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Efficient content-based image retrieval using integrated dual deep convolutional neural network Feroza D. Mirajkar; Ruksar Fatima; Shaik A. Qadeer
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 2: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i2.pp297-304

Abstract

Content-based image retrieval (CBIR) uses the content features for retrieving and searching the images in a given large database. Earlier, different hand feature descriptor designs are researched based on cues that are visual such as shape, colour, and texture used to represent these images. Although, deep learning technologies have widely been applied as an alternative to designing engineering that is dominant for over a decade. The features are automatically learnt through the data. This research work proposes integrated dual deep convolutional neural network (IDD-CNN), IDD-CNN comprises two distinctive CNN, first CNN exploits the features and further custom CNN is designed for exploiting the custom features. Moreover, a novel directed graph is designed that comprises the two blocks i.e. learning block and memory block which helps in finding the similarity among images; since this research considers the large dataset, an optimal strategy is introduced for compact features. Moreover, IDD-CNN is evaluated considering the two distinctive benchmark datasets the oxford dataset considering mean average precision (mAP) metrics and comparative analysis shows IDD-CNN outperforms the other existing model.
Co-Authors Ruksar Fatima Shaik A. Qadeer