Two-Stage Pooling Of Deep Convolutional Features For Image Retrieval

Convolutional Neural Network (CNN) based image representations have achieved high performance in image retrieval tasks. However, traditional CNN based global representations either provide high dimensional features, which incurs large memory consumption and computing cost, or inadequately capture discriminative information in images, which degenerates the functionality of CNN features. To address those issues, we propose a two-stage partial mean pooling (PMP) approach to construct compact and discriminative global feature representations. The proposed PMP is meant to tackle the limits of traditional max pooling and mean (or average) pooling. By injecting the PMP pooling strategy into the CNN based patch-level mid-level feature extraction and representation, we have significantly improved the state-of-the-art retrieval performance over several common benchmark datasets.