Image marking method based on multi-mode deep learning

Abstract

The invention discloses an image marking method based on multi-mode deep learning. The method comprises the following steps: firstly, a depth neural network is trained by utilization of images without labels; secondly, each single mode is optimized by utilization of counter propagation; finally, weights among different modes are optimized by utilization of on-line learning power gradient algorithm. The method employs a convolution neural network technology to optimize parameters of the depth neural network, and the marking precision is raised. Experiments of public data sets show that the method can raise the image marking performance effectively.

Description

technical field [0001] The invention relates to an image tagging method, in particular to an image tagging method based on multimodal deep learning, and belongs to the technical field of image processing. Background technique [0002] In recent years, with the rapid increase in the number of images, people urgently need to achieve efficient annotation of image content to achieve effective retrieval and management of large-scale images. [0003] From the perspective of pattern recognition, the image annotation problem is regarded as assigning a set of labels to images according to the content, and how to select the appropriate features to represent the image content will greatly affect the annotation performance. Due to the well-known semantic gap problem, it is difficult for existing technologies to achieve satisfactory results when semantically annotating images. In recent years, Hinton et al. proposed to use deep neural networks to efficiently train features from the trai...

AI Technical Summary

Problems solved by technology

Due to the well-known semantic gap problem, it is difficult for existing technologies to achieve satisfactory results when semantically annotating images

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