Convolutional neural networks (CNNs) have recently become the
state-of-the-art tool for large-scale image classification. In this work we
propose the use of activation features from CNNs as local descriptors for
writer identification. A global descriptor is then formed by means of GMM
supervector encoding, which is further improved by normalization with the
KL-Kernel. We evaluate our method on two publicly available datasets: the ICDAR
2013 benchmark database and the CVL dataset. While we perform comparably to the
state of the art on CVL, our proposed method yields about 0.21 absolute
improvement in terms of mAP on the challenging bilingual ICDAR dataset.

通过使用卷积神经网络的激活特征作为本地描述符，结合高斯混合模型超向量编码和 KL-Kernel 归一化，我们提出了一种用于笔迹识别的全局描述符方法，在 ICDAR 2013 基准数据库和 CVL 数据集上进行了评估，在挑战性的双语 ICDAR 数据集上改进了 0.21 个 mAP 的绝对值。

使用卷积神经网络激活特征的离线作者识别

Offline Writer Identification Using Convolutional Neural Network  Activation Features

Convolutional neural nets (convnets) trained from massive labeled datasets
have substantially improved the state-of-the-art in image classification and
object detection. However, visual understanding requires establishing
correspondence on a finer level than object category. Given their large pooling
regions and training from whole-image labels, it is not clear that convnets
derive their success from an accurate correspondence model which could be used
for precise localization. In this paper, we study the effectiveness of convnet
activation features for tasks requiring correspondence. We present evidence
that convnet features localize at a much finer scale than their receptive field
sizes, that they can be used to perform intraclass alignment as well as
conventional hand-engineered features, and that they outperform conventional
features in keypoint prediction on objects from PASCAL VOC 2011.

本研究探讨了卷积神经网络激活特征在需要对应关系的任务中的有效性，通过实验结果表明，这些特征在局部细节定位方面有更好的表现，并且在对象部分对齐和关键点预测方面优于传统的手工特征。