CAJ | 학술논문

针对现有的纹理特征提取方法计算复杂度高的问题，利用局部二值模式（LBP）算法思想简单、计算复杂度小的优势，在已有的完整LBP（CLBP）算法基础上，提出了一种改进的CLBP算法（ICLBP）。ICLBP算法保留了CLBP算法中CLBP_S，而对CLBP_M算子、CLBP_C算子进行了改进，提出一个新的纹理描述算子ICLBP_T。ICLBP算法更全面地描述了局部窗口的纹理特征，同时有效解决了CLBP算法中CLBP_M算子对灰度分布不均敏感的问题。通过对Outex、CURet数据库的数据分类实验，结果表明，相比于已有的LBP算法，ICLBP算法的分类精度有了明显的改进，同时ICLBP算法中ICLBP_SCT特征具有较低的特征维数，具有较好的实用价值。
침대현유적문리특정제취방법계산복잡도고적문제，이용국부이치모식（LBP）산법사상간단、계산복잡도소적우세，재이유적완정LBP（CLBP）산법기출상，제출료일충개진적CLBP산법（ICLBP）。ICLBP산법보류료CLBP산법중CLBP_S，이대CLBP_M산자、CLBP_C산자진행료개진，제출일개신적문리묘술산자ICLBP_T。ICLBP산법경전면지묘술료국부창구적문리특정，동시유효해결료CLBP산법중CLBP_M산자대회도분포불균민감적문제。통과대Outex、CURet수거고적수거분류실험，결과표명，상비우이유적LBP산법，ICLBP산법적분류정도유료명현적개진，동시ICLBP산법중ICLBP_SCT특정구유교저적특정유수，구유교호적실용개치。
For most texture feature extraction method, the problem of high computational complexity always exists. An Improved Complete Local Binary Pattern algorithm(ICLBP)is proposed based on Complete LBP(CLBP). ICLBP preserves the CLBP_S in CLBP, while makes an improvement on CLBP_M and CLBP_C, and proposes a new texture description operator ICLBP_T. ICLBP can describe the local texture feature in a comprehensive way, and the problem that CLBP_M operator in CLBP is sensitive to uneven distribution of gray, is well solved in ICLBP. The classification results on Outex and CURet image databases suggest that, compared to the existing LBP algorithm, ICLBP has obtained a higher classifica-tion accuracy, meanwhile, the ICLBP_SCT feature in ICLBP has a lower feature dimension and better practical value.