Multi-aspect deep learning expression-based image emotion classification method

Abstract

The invention discloses a multi-aspect deep learning expression-based image emotion classification method. The method comprises the following steps of: (1) designing an image emotion classification model: the image emotion classification model comprises a parallel convolutional neural network model and a support vector machine classifier which is used for carrying out decision fusion on network features; (2) designing a parallel convolutional neural network structure: the parallel convolutional neural network structure comprises 5 networks with same a structure, and each network comprises 5 convolutional layer groups, a full connection layer and a softmax layer; (3) carrying out significant main body extraction and HSV format conversion on an original image; (4) training the convolutional neural network model; (5) fusing image emotion features learnt and expressed by the plurality of convolutional neural networks, and training the SVM classifier to carry out decision fusion on the image emotion features; and (6) classifying user images by using the trained image emotion classification model so as to realize image emotion classification. According to the method disclosed by the invention, the obtained image emotion classification result accords with the human emotion standard, and the judgement process is free of artificial participation, so that machine-based full-automatic image emotion classification is realized.

Description

technical field [0001] The invention relates to the technical field of image classification, in particular to an image emotion classification method based on multi-faceted deep learning expression. Background technique [0002] People are easily stimulated by visual content, especially images, to generate various emotions. Image emotion classification is to classify images according to the human emotions aroused by images. The eight types of image emotions commonly used in the research are joy, awe, satisfaction, excitement, anger, disgust, fear, and sadness. Due to the complexity of images and the subjectivity of human emotions, it is a very challenging task to automatically classify image emotions by computer simulation of human high-level perception to judge image emotions. [0003] Most of the traditional image emotion classification methods adopt the method of designing manual features and constructing image emotion classifiers. Researchers extract a variety of image...

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The relationship between image information, deep learning network and learning task is

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