Quantitative comparison on dataset MSRA-1000 (N/A represents no center-bias).

<p>(<b>A</b>) Precision-Recall curves. (<b>B</b>) F-measure curves. (<b>C</b>) Precision-Recall bars.</p

Quantitative comparison for various combinations of parameters.

<p>(<b>A</b>)–(<b>C</b>) Varying σ from 0.01 to 1 with <i>α</i> = 0.95 and <i>δ</i> = 1/4: (<b>A</b>) Precision-Recall curves. (<b>B</b>) F-measure curves. (<b>C</b>) <i>P_s</i> Bars. (<b>D</b>)–(<b>F</b>) Plots of precision, recall, and F-measure for various values of σ: (<b>D</b>) <i>α</i> = 0.9, <i>δ</i> = 1/16 vs. <i>α</i> = 0.95, <i>δ</i> = 1/16. (<b>E</b>) <i>α</i> = 0.9, <i>δ</i> = 1/4 vs. <i>α</i> = 0.95, <i>δ</i> = 1/4. (<b>F</b>) <i>α</i> = 0.95, <i>δ</i> = 1/16 vs. <i>α</i> = 0.95, <i>δ</i> = 1/4.</p

Numeric comparison for various colors used in uniform quantization (%).

<p>Numeric comparison for various colors used in uniform quantization (%).</p

Visual results of our method compared with ground truth and other methods on dataset MSRA-1000.

<p>(<b>A</b>) Original images <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Liu1" target="_blank">[20]</a>. (<b>B</b>) Ground truth <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Achanta1" target="_blank">[11]</a>. (<b>C</b>) IT <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Itti1" target="_blank">[1]</a>. (<b>D</b>) SR <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Hou1" target="_blank">[14]</a>. (<b>E</b>) FT <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Achanta1" target="_blank">[11]</a>. (<b>F</b>) CA <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Goferman1" target="_blank">[19]</a>. (<b>G</b>) RC <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Cheng1" target="_blank">[10]</a>. (<b>H</b>) Ours.</p

Color space distribution and quantization.

<p>(<b>A</b>) Input image <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Liu1" target="_blank">[20]</a>. (<b>B</b>) Original color distribution of <b>A</b> in the RGB color space. (<b>C</b>) Color distribution of uniform quantization. (<b>D</b>) Color distribution of minimum variance quantization.</p

Uniform quantization vs. minimum variance quantization.

<p>(<b>A</b>) Precision-Recall curves. (<b>B</b>) Precision-Recall bars. (<b>C</b>) F-measure curves.</p

Saliency maps vs. ground truth.

<p>Given several original images <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Liu1" target="_blank">[20]</a> (<b>top</b>), our saliency detection method is used to generate saliency maps by measuring regional principal color contrasts (<b>middle</b>), which are comparable to manually labeled ground truth <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112475#pone.0112475-Achanta1" target="_blank">[11]</a> (<b>bottom</b>).</p

Saliency map with measuring two categories of spatial relationships.

<p>(<b>A</b>) Between two regions. (<b>B</b>) Between regional center and image center. (<b>C</b>) Binary segmented result simply obtained by thresholding <b>B</b> with an adaptive threshold.</p

Numeric comparison on data set MSRA-1000 (%, N/A represents without center-bias).

<p>Numeric comparison on data set MSRA-1000 (%, N/A represents without center-bias).</p

Regional principal color contrast.

<p>(<b>A</b>) Regional boundaries of using the graph-based segmentation method. (<b>B</b>) Each region represented by its principal color. (<b>C</b>) Saliency map obtained with the saliency values of regional principal colors.</p