The Circular Seal Identification Method Based on Average Relative Error

Liang, Ji; Tong, Xi; Yuan, Zi Bo

doi:10.4028/www.scientific.net/amm.513-517.4338

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…In this paper, a global pooling method based on attention was designed to replace the global average pooling in the original MLP-Mixer. The specific method is shown as Equation (7), where X is the feature map obtained from the Mixer layer feature aggregation. After pooling, we aimed to achieve a vector that is the representation of the seal impression.…”

Section: Attention-based Global Poolingmentioning

confidence: 99%

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Representation Learning Method for Circular Seal Based on Modified MLP-Mixer

Cao,

Zhou,

Zhang

et al. 2023

Entropy

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This study proposes Stamp-MLP, an enhanced seal impression representation learning technique based on MLP-Mixer. Instead of using the patch linear mapping preprocessing method, this technique uses circular seal remapping, which reserves the seals’ underlying pixel-level information. In the proposed Stamp-MLP, the average pooling is replaced by a global pooling of attention to extract the information more comprehensively. There were three classification tasks in our proposed method: categorizing the seal surface, identifying the product type, and distinguishing individual seals. The three tasks shared an identical dataset comprising 81 seals, encompassing 16 distinct seal surfaces, with each surface featuring six diverse product types. The experiment results showed that, in comparison to MLP-Mixer, VGG16, and ResNet50, the proposed Stamp-MLP achieved the highest classification accuracy (89.61%) in seal surface classification tasks with fewer training samples. Meanwhile, Stamp-MLP outperformed the others with accuracy rates of 90.68% and 91.96% in the product type and seal impression classification tasks, respectively. Moreover, Stamp-MLP had the fewest model parameters (2.67 M).

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Section: Attention-based Global Poolingmentioning

confidence: 99%

“…J.S. Liang [7] used the difference image method to perform an XOR operation on the registered seal and the questioned seal and calculated the matching similarity, but the accuracy of this method for identifying fake seals was low. Q. Guo et al used column sparsity optimization to complete the registration of seals [8].…”

Section: Introductionmentioning

confidence: 99%