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Visual Orientation Inhomogeneity Based Convolutional Neural Networks

机译：基于视觉取向的不均匀性卷积神经网络

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摘要

The details of oriented visual stimuli are better resolved when they are horizontal or vertical rather than oblique. This "oblique effect" has been researched and confirmed in numerous research studies, including behavioral studies and neurophysiological and neuroimaging findings. Although the "oblique effect" has influence in many fields, little research integrated it into computational models. In this paper, we try to explore this inhomogeneity of visual orientation based on Convolutional neural networks (CNNs) in image recognition. We validate that visual orientation inhomogeneity CNNs can achieve comparable performance with higher computational efficiency on various datasets. We can also get the conclusion that, compared with the cardinal information, oblique information is indeed less useful in natural color image recognition. Through the exploration of the proposed model on image recognition, we gain more understanding of the inhomogeneity of visual orientation. It also illuminates a wide range of opportunities for integrating the inhomogeneity of visual orientation with other computational models.
机译：当水平或垂直而不是倾斜时，定向视觉刺激的细节可以更好地解决。这种“倾斜效应”已经在许多研究中得到研究和证实，包括行为研究以及神经生理学和神经影像学发现。尽管“倾斜效应”在许多领域都有影响，但很少有研究将其整合到计算模型中。在本文中，我们尝试探索基于卷积神经网络（CNN）在图像识别中视觉定向的这种不均匀性。我们验证了视觉取向不均匀CNN可以在各种数据集上以更高的计算效率实现可比的性能。我们还可以得出结论，与基本信息相比，倾斜信息在自然彩色图像识别中的作用确实较小。通过对提出的图像识别模型的探索，我们对视觉定向的不均匀性有了更多的了解。它还为整合视觉定向的不均匀性与其他计算模型提供了广泛的机会。

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