AGE CLASSIFICATIONS BASED ON SECOND ORDER IMAGE COMPRESSED AND FUZZY REDUCED GREY LEVEL (SICFRG) MODEL
AGE CLASSIFICATIONS BASED ON SECOND ORDER IMAGE COMPRESSED AND FUZZY REDUCED GREY LEVEL (SICFRG) MODEL
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基于二阶图像压缩和模糊灰度级（SICFRG）模型的年龄分类
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Dr. B. Eswara Reddy
中科院分区：
文献类型：
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作者：
Jangala. Sasi Kiran;Dr. V. Vijaya Kumar;Dr. B. Eswara Reddy
图像分类和识别中最基本的问题之一是如何使用派生特征来描述图像。文献中的许多纹理分类和识别问题通常需要在整个图像集上进行计算，并且需要大范围的灰度值，以实现高效和精确的分类和识别。这导致在评估特征参数时的大量复杂性。为了解决这个问题，本文推导出一个二阶图像压缩和模糊降低灰度（SICFRG）模型，它减少了图像的维数和灰度范围，而没有任何损失的重要特征信息。本文推导出GLCM功能的建议SICFRG模型有效的年龄分类，分为五组的面部图像。分三个阶段推导了SICFRG年龄分类图像模式。在第一阶段中，5 × 5矩阵被压缩成2 × 2二阶子矩阵，而不会丢失任何重要属性、基元和任何其他局部属性。在第二阶段中，应用模糊逻辑来减小图像的压缩模型的灰度范围。在第三阶段中，基于图像的SICFRG模型导出GLCM。在FG-NET和Google老化数据库上的实验证据清楚地表明，该方法的分类率高于其他方法。
One of the most fundamental issues in image classification and recognition are how to characterize images using derived features. Many texture classification and recognition problems in the literature usually require the computation on entire image set and with large range of gray level values in order to achieve efficient and precise classification and recognition. This leads to lot of complexity in evaluating feature parameters. To address this, the present paper derives a Second Order image Compressed and Fuzzy Reduced Grey level (SICFRG) model, which reduces the image dimension and grey level range without any loss of significant feature information. The present paper derives GLCM features on the proposed SICFRG model for efficient age classification that classifies facial image into a five groups. The SICFRG image mode of age classification is derived in three stages. In the first stage the 5 x 5 matrix is compressed into a 2 x 2 second order sub matrix without loosing any significant attributes, primitives, and any other local properties. In stage 2 Fuzzy logic is applied tPo reduce the Gray level range of compressed model of the image. In stage 3 GLCM is derived on SICFRG model of the image. The experimental evidence on FG-NET and Google aging database clearly indicates the high classification rate of the proposed method over the other methods.