Deep Covariance Estimation Hashing for Image Retrieval

Abstract

Recently, combination of advanced convolutional neural networks and efficient hashing, deep hashing have 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. Due to convolutional features are usually high dimension and small sample size, we estimate robust covariance with matrix power normalization and then insert it 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.