关键词: 卷积神经网络, 卷积词袋（BoCW）表示, 主-辅集成分类器

Abstract: 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.

Key words: convolutional neural networks, Bag of Convolutional Words（BoCW） representation, main-auxiliary ensemble classifier