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Extensive experiments have been conducted on two benchmark image datasets.
There are some benchmark image datasets available, such as IAPR TC-12, that have proper image content descriptions.
The stimuli were rated by 20 subjects in each experiment using the ratio-scale magnitude estimation method against a benchmark image for each photo.
Extensive experiments on benchmark image dataset demonstrate that the proposed algorithm outperforms seven state-of-the-art methods on complex background images.
The whole deep architecture is well solved by the typical back propagation (BP) method and its performances are verified on three benchmark image datasets.
For all images in the Benchmark, image resizing is implemented by Seam Carving, each step we carve out five seams for facilitating the estimation of the distortion degree.
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Fig. 9 Standard benchmark images.
Fig. 4 Four standard benchmark images.
Several benchmark images are used to test the proposed technique combined of wavelet and SLLC network.
This is a set of benchmark images used as an original image in the tests.
Experimental segmentation results, using several synthetic and benchmark images, show the interest of the proposed method.
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