基于卷积词袋网络的视觉识别

近年来，卷积神经网络（CNN）凭借其强大的特征学习能力在视觉识别领域取得重要进展。针对CNN全连接层对图像平移、旋转、缩放等变换比较敏感的问题，提出了一种混合模型--卷积词袋网络（BoCW-Net）。它将BoW模型嵌入CNN结构中并代替全连接层，通过端到端的方式学习特征、字典和分类器。为实现BoCW-Net整个网络的有监督学习，提出基于方向相似度的BoCW编码。同时，为充分利用中层特征和高层特征的鉴别性，将中层辅助分类器与高层分类器集成，形成主-辅集成分类器。实验结果表明：相比全连接层，BoCW表示对各种变换具有更强的不变性;主-辅集成分类器能有效融合中层、高层特征，提高BoCW-Net的识别性能;相比新近发展的CNN模型，BoCW-Net在CIFAR-10、CIFAR-100和MNIST数据库上均取得了改进的识别性能，最终分别获得4.88%、22.48%和0.21%的测试错误率。

Bag of convolutional words networks for visual recognition

In recent years, Convolutional Neural Networks（CNN） have made a progress in visual recognition tasks with its powerful feature learning ability. A hybrid model called BoCW-Net is proposed to solve the problem that full-connection layer in CNN is more sensitive to image’s transformations such as translation, rotation and scale, et al. It embeds BoW model into CNN architectures and replaces the full-connection layer, while it can learn feature, dictionary and classifier in the end-to-end way. In order to realize supervised learning of whole BoCW-Net, BoCW encoding based on direction similarity is proposed. In the meanwhile, to take full advantage of the discrimination of both mid-level and high-level features, middle-level auxiliary classifier is integrated to high-level classifier to form the main-auxiliary ensemble classifier. Experimental results show that BoW model imbedded into CNN has better invariance for a variety of transformations compared with the full-connection layer. Main-auxiliary ensemble classifier can effectively fusion mid-level and high-level features to improve the recognition performance of BoCW-Net. Compared with the newly developed CNN models, BoCW-Net acquires improved recognition performance on CIFAR-10、CIFAR-100 and MNIST dataset with 4.88%, 22.48% and 0.21% final test error rate, respectively.