An image advanced semantic recognition method based on multi-feature fusion of a deep network

Abstract

The invention provides an image high-level semantic recognition method based on multi-feature fusion of a deep network. A global color histogram extracts the color features of an image, the LBP algorithm extracts the texture feature of the image, the deep object network extracts the object features of the image and the deep emotion network extracts the deep emotion features of the image to be fused to identify the composite emotion of the image and the main object contained, Finally, for the input image, the network model can generate descriptive phrases with high-level semantic information, including emotional semantics and object semantics. The invention starts from the aspect of depth learning for small data sets. In this invention, a new method of data expansion is proposed, which combines pre-extracted low-level features such as color and texture statistics, and proposes a multi-feature fusion method to identify the emotion of images and the high-level semantic information of objects. This method improves the accuracy of the experimental results and makes the selection of experimental images more scientific.

Description

technical field [0001] The invention relates to the technical field of computer image emotional semantic recognition, and more specifically, relates to an image advanced semantic recognition method based on deep network multi-feature fusion. Background technique [0002] Images are an important tool that can be used to convey emotions, and different forms of images will bring people different intuitive emotional experiences. Psychological research has shown that human emotions vary in response to different visual stimuli. With the development of deep learning technology, computers have made breakthroughs in processing many visual recognition tasks such as image classification, image segmentation, object detection, and scene recognition. But what about the emotion the image inspires? Whether it is also possible to form judgments similar to humans through deep learning methods. In fact, due to the subjectivity and complexity of emotions, identifying evoked emotions from ima...

AI Technical Summary

Problems solved by technology

[0005] However, the current research does not fully reflect the high-level semantic information contained in the image. In addition, the number of high-quality emotional data sets is still small, which cannot fully meet the training requirements of deep learning.

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