This work investigates the potential of seam carving as a feature pooling technique within Convolutional Neural Networks (CNNs) for image classification tasks. We propose replacing the traditional max pooling layer with a seam carving operation. Our experiments on the Caltech-UCSD Birds 200-2011 dataset demonstrate that the seam carving-based CNN achieves better performance compared to the model utilizing max pooling, based on metrics such as accuracy, precision, recall, and F1-score. We further analyze the behavior of both approaches through feature map visualizations, suggesting that seam carving might preserve more structural information during the pooling process. Additionally, we discuss the limitations of our approach and propose potential future directions for research.

翻译：本研究探讨了将接缝裁剪作为一种特征池化技术应用于卷积神经网络（CNN）以完成图像分类任务的潜力。我们提出用接缝裁剪操作替代传统的最大池化层。在Caltech-UCSD Birds 200-2011数据集上的实验表明，基于接缝裁剪的CNN模型在准确率、精确率、召回率和F1分数等指标上，相较于使用最大池化的模型取得了更优的性能。我们进一步通过特征图可视化分析两种方法的行为，结果表明接缝裁剪在池化过程中可能保留了更多的结构信息。此外，我们讨论了该方法的局限性，并提出了未来潜在的研究方向。