Rotation Invariance Analysis Of Local Convolutional Features In Image Retrieval

Recently, local features computed using convolutional neural networks (CNNs) show good performance to image retrieval. However, the local convolutional features obtained by the CNNs (LC features) are inherently sensitive to rotation perturbations. This leads to miss-judgements in retrieval tasks. In this work, our objective is to enhance the robustness of LC features against image rotation. To do this, we conduct a thorough experimental evaluation of two candidate anti-rotation strategies (in-model data augmentation, and post-model feature augmentation), over two kinds of rotation attacks (dataset attack and query attack). We end up a series of good practices with steady quantitative supports, which lead to the best strategy for computing LC features with high rotation invariance in image retrieval.