Review Article
Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review
Table 4
A summary of the performance of local feature-based approaches for CBIR.
| Author | Application | Method | Dataset | Accuracy |
| Kang et al. [64] | Image similarity assessment | Feature-based sparse representation | COIL-20 | 0.985 | Zhao et al. [65] | Semisupervised image annotation | Cooperative sparse representation | ImageCLEF-VCDT | — | Thiagarajan et al. [66] | Image retrieval | Supervised local sparse coding of sub image feature | Cambridge image dataset | 0.97 | Hong and Zhu [67] | Transductive learning image retrieval | Hypergraph-based multiexample ranking | Yale face dataset | 0.65 | Wang et al. [68] | Retrieval-based face annotation | Weak label regularized local coordinate coding | Databases “WDB,” “ADB” | — | Srinivas et al. [69] | Content-based medical image retrieval | Dictionary learning | ImageCLEF dataset | 0.5 | Mohamadzadeh and Farsi [70] | Content-based image retrieval system | Sparse representation | Flower dataset, Corel dataset | — | Li et al. [71] | Sketch-based image retrieval | SBIR framework based on product quantization (PQ) with sparse coding | Eitz benchmark dataset | 0.98 | Duan et al. [73] | Face recognition | Context-aware local binary feature learning | LFW, YTF, FERET | 0.846 |
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