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Heuristic image scale normalization method based on attention mechanism

A technology of attention and normalization, applied in the fields of image recognition, convolutional neural network, and computer vision, can solve problems such as scaling and deformation, and achieve the effect of reducing waste and avoiding unfavorable changes

Active Publication Date: 2021-08-24
ZHEJIANG UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to improve the deficiencies of the current common image scale normalization methods and avoid the scaling and deformation problems caused by inconsistent image aspect ratios. The present invention proposes a heuristic image scale based on attention mechanism Normalization method

Method used

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  • Heuristic image scale normalization method based on attention mechanism
  • Heuristic image scale normalization method based on attention mechanism
  • Heuristic image scale normalization method based on attention mechanism

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Embodiment

[0047] An auxiliary classification network is trained through the ImageNet dataset. The auxiliary attention feature map generation network consists of multiple convolutional layers. The generation network is an encoding and decoding structure, including convolutional layers, ResBlock and transposed convolutional layers. The backbone classification network uses AlexNet with a high operating speed.

[0048] By applying this network to our normalized image, we get the final normalized result. Specific examples can refer to figure 2 .

[0049] We then experimented with our normalization method on an image recognition task. The data set used in this experiment is the data set of Chinese herbal medicines, which contains 15485 pictures and a total of 556 types of medicinal materials.

[0050] We use three ways to normalize the image scale (direct scaling, random cropping, our method). Then use the normalized pictures to conduct experiments to obtain the classification accuracy o...

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Abstract

The invention discloses a heuristic image scale normalization algorithm based on an attention mechanism. According to the method, firstly, a data set with a large amount of data is used for training a classification network with an attention mechanism, and the network can output an attention feature map and an image classification result at the same time; the network is trained by using a classification result in a training process. According to the method, an attention feature map has the capability of quantifying the importance degree of pixels in an image, and a sliding window is used for sliding on the attention feature map. When the value in the sliding window is the maximum value, the position corresponding to the sliding window is the to-be-cut position of the original image. According to the method, the image can be converted into the specified size without deformation. When the image needs to be cut, the method can automatically select a proper area for cutting.

Description

technical field [0001] The invention relates to the fields of computer vision, image recognition, and convolutional neural network, in particular to a heuristic image scale normalization method based on an attention mechanism. Background technique [0002] There are currently many image recognition related technologies that use deep learning models. Such deep learning-based image recognition models usually require the input image to have a fixed size. Generally speaking, when the size of the image to be input does not match the size required by the model, the image to be input will be directly scaled to match the size received by the model. However, when the aspect ratio of the image received by the model is inconsistent with that of the image to be recognized, scaling the image to be recognized will cause distortion. This will adversely affect the model's predictions. To solve the above problems, a heuristic image size normalization method based on attention mechanism is...

Claims

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Application Information

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IPC IPC(8): G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V10/25G06N3/048G06N3/045G06F18/213
Inventor 张引董建洲
Owner ZHEJIANG UNIV
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