Review Article
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%) |
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