Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (12): 3536-3546.doi: 10.13229/j.cnki.jdxbgxb.20220048

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Ordinal cross entropy Hashing based on generative adversarial network

Zhen WANG1,2(),Xiao-han YANG1,Nan-nan WU1,Guo-kun LI1,Chuang FENG1   

  1. 1.School of Computer Science and Technology，Shandong University of Technology，Zibo 255022，China
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education，Jilin University，Changchun 130012，China
  • Received:2022-01-11 Online:2023-12-01 Published:2024-01-12

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Abstract:

To fast respond the large scale RS image search task， the deep generative adversarial learning ordinal cross entropy Hashing （GANOCH） was proposed. Firstly， the ordinal relation preserving objective function was established based on cross entropy， then the triplet similarity relationship owned the same probability distribution in different spaces. As a result， the ability of preserving ordinal relation is enhanced. Then， to fix the NP hard problem， the discrete binary encoding was relaxed to the continuous representation. Furthermore， the quantization loss function was designed based on triplet ordinal cross entropy and the loss caused by the continuous mechanism was minimized. Finally， a supervised deep generative adversarial network was established to synthesize RS image dataset according to both label matrix and random noise. Thus， the over fitting problem is avoided. The final ANN search comparative experiments were conducted on three large scale datasets， and the proposed GANOCH achieved the best performance.

Key words: computer application technology, Hashing algorithm, cross entropy, ordinal relation preserving

CLC Number: 

  • TP391.41

Fig.1

Flowchart of generative adversarial learning ordinal cross entropy Hashing"

Fig.2

Recall curves on SAT4 dataset of various algorithms"

Fig.3

Recall curves on SAT6 dataset of various algorithms"

Fig.4

Recall curves on UCMD dataset of various algorithms"

Table 1

mAP values of neighbor retrieval performance of different algorithms on the SAT4 dataset"

长度/bitGANOCHTBHDCHPRHKMHITQSHLSH
640.59340.57680.48620.43610.39460.36570.34820.3407
1280.63750.61240.49860.45280.41730.38560.37240.3615
2560.65170.63450.51280.48570.43610.42850.41520.3986

Table 2

mAP values of neighbor retrieval performance of different algorithms on the SAT6 dataset"

长度/bitGANOCHTBHDCHPRHKMHITQSHLSH
640.60420.58260.49360.45860.41250.37640.36950.3628
1280.64390.62680.51740.47950.42810.39270.38640.3752
2560.68150.65270.53940.49720.45160.43590.42380.4175

Table 3

mAP values of neighbor retrieval performance of different algorithms on the UCMD dataset"

长度/bitGANOCHTBHDCHPRHKMHITQSHLSH
640.36270.34150.29170.24620.21350.19860.17240.1637
1280.48520.46380.39630.35270.28160.24620.20150.1842
2560.53940.49750.43190.37460.31680.26730.23510.2148

Fig.5

Comparison of ablation experiments results."

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