Image emotion classification method based on LSTM network and attention mechanism

Abstract

The invention discloses an image emotion classification method based on an LSTM network and an attention mechanism. The method comprises the steps of initializing an original image; setting an image emotion label classifier based on an LSTM network and an attention mechanism; training an image emotion label classifier; testing an image emotion label classifier; and performing sentiment classification on the target image by using the tested image sentiment label classifier to obtain a sentiment classification result. According to the method, image emotion attributes are introduced, an attention mechanism acts on a visual feature map of an image, weighted features of the emotion attributes and the visual features are obtained through calculation and serve as the initial state of an LSTM network, and therefore image emotion areas are accurately detected, and meanwhile multiple emotion areas of the image are focused as much as possible; and based on the image emotion regions, emotion classification is carried out on the image through a classifier, so that image emotion prediction is more accurate, and an image emotion classification result obtained through the method conforms to a human emotion standard.

Description

Technical field [0001] The present invention relates to the technical field of image processing, and more specifically, to an image emotion classification method based on an LSTM network and an attention mechanism. Background technique [0002] At present, people at home and abroad have begun to research and explore image emotion classification. At present, the general way of image emotion classification is to select the image to be studied, extract the visual features of the image, establish the emotional space, and select the appropriate classifier to study. The images are trained first and then classified. However, in the visual task of image sentiment analysis, the attention system that affects humans is often the local area of ​​the image rather than the overall area of ​​the image, and the existing image sentiment classification model is mainly based on the overall area of ​​the image, which leads to the unsatisfactory effect of emotion classification. . Summary of the in...

AI Technical Summary

Problems solved by technology

[0003] In order to solve the problem of low accuracy of existing image emotion classification methods, the present invention provides an image emotion classification method based on LSTM network and attention mechanism

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