Adaptive Weighted 3D Object Image Inference Model Based on Image Complexity

Yueqi Liu, Pu Meng, Zhuoyue Diao, Xin Meng, Liqun Zhang, Xiaodong Li
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Abstract

The research on product style classification based on CNN is very active, but the data used to train CNN(Convolutional Neural Networks) are often single-view images of 3D objects, which will lead to the loss of unpredictable object feature information and does not match the real scene. It reduces the quality of the model training. This paper proposes an adaptive weighted CNN model based on image complexity. Feature extraction is performed on images of 3D objects from different perspectives through convolutional neural networks, and the final classification result is obtained by weighting based on image complexity. The 3D object discrimination model in this paper is more in line with the cognitive process of the audience, and can improve the quality of style inference of 3D objects.
基于图像复杂度的自适应加权三维目标图像推理模型
基于CNN的产品风格分类研究非常活跃,但训练CNN(卷积神经网络)所用的数据往往是3D物体的单视图图像,这会导致丢失不可预测的物体特征信息,与真实场景不匹配。它降低了模型训练的质量。提出了一种基于图像复杂度的自适应加权CNN模型。通过卷积神经网络对不同角度的三维物体图像进行特征提取,并根据图像复杂度加权得到最终的分类结果。本文所建立的三维物体识别模型更符合受众的认知过程,可以提高三维物体风格推理的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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