Color and structure are the two pillars that combine to give an image its meaning. Interested in critical structures for neural network recognition, we isolate the influence of colors by limiting the color space to just a few bits, and find structures that enable network recognition under such constraints. To this end, we propose a color quantization network, ColorCNN, which learns to structure an image in limited color spaces by minimizing the classification loss. Building upon the architecture and insights of ColorCNN, we introduce ColorCNN+, which supports multiple color space size configurations, and addresses the previous issues of poor recognition accuracy and undesirable visual fidelity under large color spaces. Via a novel imitation learning approach, ColorCNN+ learns to cluster colors like traditional color quantization methods. This reduces overfitting and helps both visual fidelity and recognition accuracy under large color spaces. Experiments verify that ColorCNN+ achieves very competitive results under most circumstances, preserving both key structures for network recognition and visual fidelity with accurate colors. We further discuss differences between key structures and accurate colors, and their specific contributions to network recognition. For potential applications, we show that ColorCNNs can be used as image compression methods for network recognition.

翻译：颜色和结构是使图像具有含义的两大支柱。 对神经网络识别的关键结构感兴趣, 我们通过将颜色空间限制在几个位数上, 孤立颜色的影响, 并找到能够使网络在这种限制下得到承认的结构。 为此, 我们提出一个颜色量化网络, 即ColorCNN, 它通过分类损失最小化来学习在有限的颜色空间构建图像。 在ColorCNNN的架构和洞察的基础上, 我们引入ColorCNN+, 它支持多色空间大小配置, 并解决以前在大彩色空间下的识别不准确性和不可取视觉忠诚问题。 我们进一步探讨以前的关键结构与准确颜色之间的差异, 以及它们对于网络识别的具体贡献。 对于潜在的应用, ColorCNN+ 学习了一种新式的模仿方法, 这减少了在大彩色空间下对颜色的过度匹配, 有助于视觉真实性和识别准确性。 实验证实ColorCNN+在多数情况下都取得了非常有竞争力的结果, 保存网络识别和直观真实性的关键结构。 我们进一步讨论关键结构与准确性之间的差异, 以及它们对于网络识别的具体贡献。 对于网络识别作用, 我们展示了ClocCNNs 的识别可以用作压缩图像的识别方法。