Bag-of-words (BoW) models are widely used in the field of computer vision. A BoW model consists of a visual vocabulary that is generated by unsupervised clustering the features of the training data, e.g., by using kMeans. The clustering methods, however, struggle with large amounts of data, in particular, in the context of action recognition. In this paper, we propose a transformation of the standard BoW model into a neural network, enabling discriminative training of the visual vocabulary on large action recognition datasets. We show that our model is equivalent to the original BoW model but allows for the application of supervised neural network training. Our model outperforms the conventional BoW model and sparse coding methods on recent action recognition benchmarks.
Richard and Gall REF converted the kMeans-based BoW model into an equivalent recurrent neural network and trained the BoW model and classifier together.
A BoW-equivalent Recurrent Neural Network for Action Recognition.
BMVC
Computer Science