TGSIFT: Robust SIFT Descriptor Based on Tensor Gradient for Hyperspectral Images

Li, Yanshan; Fan, Leidong; Xie, Weixin

doi:10.1049/cje.2020.08.007

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…A large number of scholars have conducted in-depth discussion and analysis on the application and improvement of SIFT. Combining tensor theory and based on spatial and spectral information, LI et al proposed a tensor gradient SIFT and applied it to feature extraction and matching of hyperspectral images [16]. Li J et al used PCA and SIFT to predict protein-protein interactions in organisms through protein sequences, and verified by five-fold cross-validation method, the accuracy of this method exceeded 97% [17].…”

Section: Related Workmentioning

confidence: 99%

Application Research of Trademark Recognition Technology based on SIFT Feature Recognition Algorithm in Advertising Design

Zhang¹

2022

IJACSA

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Now-a-days, due to the sharp increase in the number of advertising designs, creative duplication is easy to occur in advertising design. If this situation is not discovered in time, it may cause legal disputes and cause damage to the reputation and property of the enterprise. In view of the above situation, this paper proposes a trademark recognition technology based on SIFT feature recognition algorithm to avoid duplication of advertisement design and cause copyright disputes. Aiming at the defect that the dimension of the image feature vector extracted by SIFT algorithm is too high, the principal component analysis method is used to reduce its dimension. For the problem of unsatisfactory image recognition rate accuracy of SIFT algorithm, a support vector machine is used to classify the extracted feature vector, so as to improve the image recognition rate. Based on the above content, build a trademark recognition model. The research results show that the recognition accuracy of the model reaches 98.82%, 0.66% and 0.58% higher than that of Model 1 and Model 2; AUC value of model 3 is 0.962, 0.039 higher than model 2 and 0.107 higher than model 1. The above results show that the proposed trademark recognition model can better identify similar advertising designs, thereby avoiding design duplication and legal disputes.

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Section: Related Workmentioning

confidence: 99%

Application Research of Trademark Recognition Technology based on SIFT Feature Recognition Algorithm in Advertising Design

Zhang¹

2022

IJACSA

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“…Shi and Pun [33] built multiscale RESNET to realize target detection. Li et al [34] detected targets based on boundary features. e above algorithms analyze the target from the perspective of morphology to achieve target detection.…”

Section: Target Detection Effectmentioning

confidence: 99%

Material Discrimination Algorithm Based on Hyperspectral Image

Zhou¹,

Wang

Jiao³

et al. 2021

Scientific Programming

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Hyperspectral information can be used to express the material properties of objects, which has a strong effect on camouflage recognition. However, it is difficult to process it directly because of the huge hyperspectral image data. Therefore, this paper proposes a new band selection algorithm to achieve band selection by simulating visual perception. The subspace clustering self-attention adversarial network is constructed to realize the initial selection of band. According to the visual chromatic aberration principle, a model is constructed to determine the band that combines the strongest response intensity of a particular material, and then this band is selected as the final band, therefore realizing the algorithm of material demarcation in this way.

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Multidimensional Local Binary Pattern for Hyperspectral Image Classification

Tang

Xie

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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TGSIFT: Robust SIFT Descriptor Based on Tensor Gradient for Hyperspectral Images

Cited by 4 publications

References 29 publications

Application Research of Trademark Recognition Technology based on SIFT Feature Recognition Algorithm in Advertising Design

Application Research of Trademark Recognition Technology based on SIFT Feature Recognition Algorithm in Advertising Design

Material Discrimination Algorithm Based on Hyperspectral Image

Multidimensional Local Binary Pattern for Hyperspectral Image Classification

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