Mathematical Problems in Engineering / 2019 / Article / Tab 4

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.

AuthorApplicationMethodDatasetAccuracy

Kang et al. [64]Image similarity assessmentFeature-based sparse representationCOIL-200.985
Zhao et al. [65]Semisupervised image annotationCooperative sparse representationImageCLEF-VCDT
Thiagarajan et al. [66]Image retrievalSupervised local sparse coding of sub image featureCambridge image dataset0.97
Hong and Zhu [67]Transductive learning image retrievalHypergraph-based multiexample rankingYale face dataset0.65
Wang et al. [68]Retrieval-based face annotationWeak label regularized local coordinate codingDatabases “WDB,” “ADB”
Srinivas et al. [69]Content-based medical image retrievalDictionary learningImageCLEF dataset0.5
Mohamadzadeh and Farsi [70]Content-based image retrieval systemSparse representationFlower dataset, Corel dataset
Li et al. [71]Sketch-based image retrievalSBIR framework based on product quantization (PQ) with sparse codingEitz benchmark dataset0.98
Duan et al. [73]Face recognitionContext-aware local binary feature learningLFW, YTF, FERET0.846