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Good Choices for Deep Convolutional Feature Encoding

Deep convolutional neural networks can be used to produce discriminative image level features. However, when they are used as the feature extractor in a feature encoding pipeline, there are many design choices that are need to be made. In this work, we conduct a comprehensive study on deep convolutional feature encoding, by paying a special attention on its feature extraction aspect. We mainly evaluated the choices of the encoding methods; the choices of the base DCNN models; and the choices of the data augmentation methods. We not only quantitatively confirmed some known and previously unknown good choices for deep convolutional feature encoding, but also found out that some known good choices tune out to be bad. Base on the observations in the experiments, we present a very simple deep feature encoding pipeline, and confirmed its state-of-the-art performances on multiple image recognition datasets.