2020
DOI: 10.23919/jsee.2020.000052
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A multivariate grey incidence model for different scale data based on spatial pyramid pooling

Abstract: In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling. Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly, Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices … Show more

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Cited by 14 publications
(6 citation statements)
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“…When the selected pooling value is the max value in the pooling domain, the calculation process is equivalent to max pooling operation. Seventh, spatial pyramid pooling [ 33 ] operation performs max pooling operations on feature maps at three different scales. It replaces the last pooling layer with a spatial pyramid pooling layer, which can process information of different scales in the image to generate the fixed length feature information.…”
Section: Variable Of Pooling Kernelmentioning
confidence: 99%
“…When the selected pooling value is the max value in the pooling domain, the calculation process is equivalent to max pooling operation. Seventh, spatial pyramid pooling [ 33 ] operation performs max pooling operations on feature maps at three different scales. It replaces the last pooling layer with a spatial pyramid pooling layer, which can process information of different scales in the image to generate the fixed length feature information.…”
Section: Variable Of Pooling Kernelmentioning
confidence: 99%
“…The proposed network structure is shown in figure 1. Figure 2 is the spatial pyramid pooling (SPP) module [23,24], and figure 3 is the flow chart of the algorithm in this paper. In YOLOv3, the mean square error (MSE) is used as the loss function to regression the center point, width and height of BBox, but in this way, the coordinate value of each point of BBox is regarded as an independent variable, without considering the integrity of the object frame, and the n l -norm is sensitive to the scale of the object.…”
Section: Central Coordinate Lossmentioning
confidence: 99%
“…The Spatial Pyramid Pooling (SPP) structure was proposed by HeKaiming et al [9], it is proposed to enrich the methods of CNN target detection. Among the layers included in the CNN structure, the convolutional pooling layer does not care about the size of the input image, but the fully-connected layer requires an image of the same dimension.…”
Section: Introduction Of Spatial Pyramidal Pooling Layermentioning
confidence: 99%