An improved deep hashing model for image retrieval with binary code similarities

Journal of Big Data - Tập 11 Số 1
Huawen Liu1, Zongda Wu1, Minghao Yin2, Dunshan Yu1, Xiaoyan Zhu3, Jungang Lou4
1Department of Computer Science, Shaoxing University, 508 West Huancheng Road, Shaoxing, 312000, People's Republic of China
2School of Information Science and Technology, Northeast Normal University, Changchun, People’s Republic of China
3School of Computer Science and Technology, Zhejiang Normal University, Jinhua, People’s Republic of China
4School of Information Engineering, Huzhou University, Huzhou, People’s Republic of China

Tóm tắt

AbstractThe exponential growth of data raises an unprecedented challenge in data analysis: how to retrieve interesting information from such large-scale data. Hash learning is a promising solution to address this challenge, because it may bring many potential advantages, such as extremely high efficiency and low storage cost, after projecting high-dimensional data to compact binary codes. However, traditional hash learning algorithms often suffer from the problem of semantic inconsistency, where images with similar semantic features may have different binary codes. In this paper, we propose a novel end-to-end deep hashing method based on the similarities of binary codes, dubbed CSDH (Code Similarity-based Deep Hashing), for image retrieval. Specifically, it extracts deep features from images to capture semantic information using a pre-trained deep convolutional neural network. Additionally, a hidden and fully connected layer is attached at the end of the deep network to derive hash bits by virtue of an activation function. To preserve the semantic consistency of images, a loss function has been introduced. It takes the label similarities, as well as the Hamming embedding distances, into consideration. By doing so, CSDH can learn more compact and powerful hash codes, which not only can preserve semantic similarity but also have small Hamming distances between similar images. To verify the effectiveness of CSDH, we evaluate CSDH on two public benchmark image collections, i.e., CIFAR-10 and NUS-WIDE, with five classic shallow hashing models and six popular deep hashing ones. The experimental results show that CSDH can achieve competitive performance to the popular deep hashing algorithms.

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Journal of Big Data

Tập 11 Số 1

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