A Color Texture Image Segmentation Method Based on Fuzzy c-Means Clustering and Region-Level Markov Random Field Model

<div class="floats-full-page floats-full-page-other"><h2>Table 4</h2><div class="floats-full-page-caption">Comparison accuracies of four different algorithms on the high resolution remote sensing images (acc/kc).</div><div class="floats-full-page-content"><table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr /></td></tr><tr class="thead"><td align="left"> </td><td align="center">Figure <a href="../fig3/#a">3(a)</a></td><td align="center">Figure <a href="../fig4/#a">4(a)</a></td><td align="center">Average</td></tr><tr><td class="thead-hr" colspan="4"><hr /></td></tr><tr><td align="left">FLICM</td><td align="center">0.5129/0.2566</td><td align="center">0.6379/0.4909</td><td align="center">0.5754/0.3738</td></tr><tr><td align="left">HMRF-FCM</td><td align="center">0.6340/0.4692</td><td align="center">0.7112/0.5904</td><td align="center">0.6726/0.5298</td></tr><tr><td align="left">RICM</td><td align="center">0.8638/0.7856</td><td align="center">0.7850/0.6935</td><td align="center">0.7936/0.7395</td></tr><tr><td align="left">RFLICM</td><td align="center"><b>0.8810/0.8121</b></td><td align="center"><b>0.8734/0.8148</b></td><td align="center"><b>0.8772/0.8135</b></td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr" /></td></tr></table></td></tr></table></div></div>

Table 4 | A Color Texture Image Segmentation Method Based on Fuzzy c-Means Clustering and Region-Level Markov Random Field Model

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Table 4: A Color Texture Image Segmentation Method Based on Fuzzy c-Means Clustering and Region-Level Markov Random Field Model

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Table 4

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