Deep Covariance Estimation Hashing - Yue Wu (IEEE Access, 2019)
Deep hashing, the combination of advanced convolutional neural networks and efficient hashing, has recently achieved impressive performance for image retrieval. However, state-of-the-art deep hashing methods mainly focus on constructing hash function, loss function and training strategies to preserve semantic similarity. For the fundamental image characteristics, they depend heavily on the first-order convolutional feature statistics, failing to take their global structure into consideration. To address this problem, we present a deep covariance estimation hashing (DCEH) method with robust covariance form to improve hash code quality. The core of DCEH involves covariance pooling as deep hashing representation, performing global pairwise feature interactions. The covariance pooling can capture richer statistic information of deep convolutional features and produce more informative global representations.Due to convolutional features are usually high dimension and small sample size, we estimate robust covariance by shrinking its eigenvalues using power normalization, forming an independent structural layer. Then the structural layer is embedded into deep hashing paradigm in an end-to-end learning manner. Extensive experiments on three benchmarks show that the proposed DCEH outperforms its counterparts and achieves superior performance.