Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

Table 5

A summary of the performance of deep-learning-based approaches for CBIR.

Authors	Datasets	Purpose	Model	Accuracy
Krizhevsky et al. [87]	ILSVRC-2010 and ILSVRC-2012	Image classification	CNN	37.50% top-1 and 17.00% top-5 error rate on ILSVRC-2010 and 15.3% top-5 error rate on ILSVRC-2012
Sun et al. [88]	LFW (Labeled Face in the Wild)	Face verification	ConvNets DeepID	97.45% accuracy
Karpathy and Fei-Fei [89]	Flickr8K, Flickr30 K and MSCOCO	Generation of descriptions of image regions	CNN and multimodal RNN	Encouraging results
Li et al. [90]	MIRFlickr and NUS-WIDE	Social image understanding	DCE	The performance of CBIR 0.512 on MIRFlickr and 0.632 NUS-WID with k = 1000
Kondylidis et al. [82]	INRIA Holidays, Oxford 5k, Paris 6k, UK Bench	Content-based image retrieval	CNN based tf-idf	Improved results
Shi et al. [83]	5356 skeletal muscle and 2176 lung cancer images with four types of diseases	Histopathology image classification and retrieval	PDRH algorithm	97.49% classification accuracy and MAP (97.49% and 97.33%)