为了解决二维图标形状视觉复杂度的计算度量问题提出一个基于回归模型的图标形状复杂度计算模型.r首先对图标训练数据集进行测试者心理评估;然后对该数据集进行几何特征抽取并计算得到候选特征变量;最后r通过回归分析从候选特征变量中选出4个变量构建回归模型来量化评估图标复杂度.用图标测试数据集对该回归模r型进行验证的结果表明该模型可以解释80%的复杂度人工评估结果;测试数据集的模型量化评估结果和人工评估r结果之间斯皮尔曼相关系数达0.922(最大值为1).该模型在图标形状分析、检索、分类等方面具有广泛应用价值.%Quantitative evaluation of the visual complexity of 2D logos aims at reproducing manual evaluation of logos complexity using a model based on various contributing variables. This paper presents a model capable of evaluating logo shape complexities based on regression analysis. We started from manual evaluation of logo training data, and then proposed several variables of geometric features to numerically measure logo complexities from different perspectives. Finally, we selected four variables among given 17 candidates of variables through regression analysis to obtain a model for measuring logo complexities. Experimental results show that our model is able to explain 80% of the results of logo complexity evaluated manually. The validation results of a new logo testing data set show that the Spearman correlation coefficient between the model evaluation and manual evalua-tion of logo testing data is 0.922 (the maximum value is 1). Potential applications of our work range from logo shape analysis, logo retrieval to logo classifications.
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