Review Article
Image Retrieval Using Low Level and Local Features Contents: A Comprehensive Review
Table 2
Summary of texture feature-based image retrieval techniques.
| Year | Method | Similarity measure | Dataset | Performance measure (%) |
| 1997 | Subblock DCT DC and AC coefficients [31] | Modified Euclidean distance | 200 images of woods, flowers, and sky with mountains | Average retrieval rate (ARR): 82 |
| 2017 | Multiresolution RGB images, feature vector generated with DCT DC coefficients, and statistical features from the group of AC coefficients [32] | Euclidean distance | Corel-1K | Precision: 87.50 | Recall: 17.50 | F score: 29.16 | GHIM-10K | Precision: 82.50 | Recall: 3.30 | F score: 6.35 |
| 2013 | Quantized histogram statistical texture features generation using DCT with DC and first 3 AC components [33] | Euclidean distance | Corel-1K | Precision: 80 | Recall: 81 | F score: 80 |
| 2000 | Wavelet-based salient points, color moments, and Gabor moments of salient points [24] | Mean square error | COREL | Retrieval accuracy: 83.2 |
| 2017 | LTrP and DWT with the artificial neural network [34] | Artificial neural network | Corel-1K | ARR: 97.9 | Corel-5k | ARR: 87.42 | Corel-10K | ARR: 74.13 |
| 2008 | Curvelet transform with low-order statistics [25] | L2 distance | Brodatz texture database | (ARR: 79.54 |
| 2012 | Ranklet transform and color moments with K-means clustering [35] | Euclidean distance | Wang dataset | Precision: 78.86 |
| 2012 | Second-order local tetra patterns using vertical and horizontal derivatives of pixels direction [22] | Modified Canberra | Corel-1000 | Precision: 75.9 | Recall: 48.7 | Brodatz texture database | Recall: 85.3 | MIT VisTex | Recall: 90.02 |
| 2012 | Edge directional information feature based on local extrema [28] | Modified Canberra | Corel-5000 | Precision: 48.8 | Recall: 21.1 | Corel-10K | Precision: 40.0 | Recall: 15.7 | Corel-1000 | Precision:74.8 | Recall: 49.16 | Brodatz texture | Recall: 82.68 |
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