Fine-grained image classification method and system based on attention mechanism and cutting filling

Abstract

The invention relates to the field of deep learning and the field of image classification, in particular to a fine-grained image classification method based on an attention mechanism and cutting filling, and the method comprises the steps: constructing a convolutional neural network model; inputting an original image into a convolutional neural network model, and combining an attention mechanism to obtain an attention image; cutting a concerned image to obtain a sub-image, filling the sub-image, and performing down-sampling to obtain a filled image; inputting the attention image and the filling image into a convolutional neural network model, and obtaining probability values of corresponding categories through a linear layer and a softmax classifier; selecting a maximum probability value,and judging a classification result according to the maximum probability value; marking a classification label on the original image according to the result. The concerned image of the original imageis segmented and then subjected to filling processing, so that the correlation among all parts is destroyed, the network features local features, high-level semantics are not destroyed, and the use and training time of parameters is greatly reduced.

Description

technical field [0001] The present invention relates to the field of deep learning and image classification, in particular to a fine-grained image classification method and system based on attention mechanism and cut-and-fill. Background technique [0002] Fine-grained image classification is an important task in the field of computer vision, and this task is very challenging. Fine-grained image classification is different from general-purpose object recognition, because coarsely observed fine-grained objects (such as birds, car models, and airplanes, etc.) are visually similar, so fine-grained image classification is very dependent on the locality of the object feature. General classification methods are also applicable to fine-grained image classification, so how to better learn local features is the key to fine-grained image classification. [0003] In recent years, the attention mechanism has been widely used in fine-grained image classification networks, such as the e...

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Problems solved by technology

[0004] However, this method destroys the spatial structure of the object and causes certain damage to the high-level semantic information. At the same time, the use of the class activation map requires the training of category weights to obtain additional attention parts to suppress other categories, resulting in image classification. There is an error

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