An Image Annotation Method Based on Multimodal Deep Learning

Abstract

The invention discloses an image labeling method based on multi-modal deep learning. The method comprises the following steps: firstly, using unlabeled images to train a deep neural network; secondly, using backpropagation to optimize each single mode; finally, using online The learned power gradient algorithm optimizes the weights across different modalities. The invention optimizes the parameters of the deep neural network by applying the convolutional neural network technology, and improves the labeling accuracy. Experiments on public datasets show that the invention can effectively improve the performance of image annotation.

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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