Hash Image Retrieval Based on Category Similarity Feature Expansion and Center Triplet Loss
PAN Lili1, MA Junyong1, XIONG Siyu1, DENG Zhimao1, HU Qinghua2
1. College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004; 2. College of Intelligence and Computing, Tianjin University, Tianjin 300350
Abstract Convolutional neural networks are commonly employed in the existing deep hashing image retrieval methods. The similarity representation of the deep features extracted by convolutional neural networks is insufficient. In addition, the local triplet samples are mainly constructed for triplet deep hashing from the small batch data, the size of the local triplet samples is small and the data distribution is lack of globality. Consequently, the network training is insufficient and the convergence is difficult. To address these issues, a model of hash image retrieval based on category similarity feature expansion and center triplet loss is proposed. A hash feature extraction module based on vision transformer is designed to extract global feature information with stronger representation ability. To expand the size of mini-batch training samples, a similar feature expansion module based on category constraint is put forward. New feature is generated by the similarity among samples of the same category to enrich the triplet training samples. To enhance the global ability of triplet loss, a center triplet loss function based on Hadamard(CTLH) is constructed. Hadamard is utilized to establish the global hash center constraint for each class. With CLTH, the learning and the convergence of the network are accelerated by adding the center triplet of local constraint and global center constraint, and the accuracy of image retrieval is improved. Experiments on CIFAR10 and NUS-WIDE datasets show that HRFT-Net gains better mean average precision for image retrieval with different bit lengths of hash code, and the effectiveness of HRFT-Net is demonstrated.
Fund:General Program of Natural Science Foundation of Hunan Province(No.2021JJ31164), Key Program of Science Research Foundation of Education Department of Hunan Province(No.22A0195)
Corresponding Authors:
PAN Lili, Ph.D., associate professor. Her research interests include image processing and deep learning.
About author:: MA Junyong, master. His research interests include image processing and deep learning.XIONG Siyu, master student. Her research interests include image processing and deep learning.DENG Zhimao, master student. His research interests include image processing and deep learning.HU Qinghua, Ph.D., professor. His research interests include uncertainty modeling for big data, machine learning for multi?modal data and intelligent unmanned systems.
PAN Lili,MA Junyong,XIONG Siyu等. Hash Image Retrieval Based on Category Similarity Feature Expansion and Center Triplet Loss[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(8): 685-700.
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2023, Vol. 36 
