Picture feature extraction method and device, storage medium and computer equipment

Abstract

The invention discloses a picture feature extraction method and device, a storage medium and computer equipment, relates to the technical field of information, and mainly aims to improve the picture feature extraction precision. The method comprises the following steps: acquiring a to-be-processed picture in an actual service scene; segmenting the picture to be processed into a plurality of sub-pictures, and determining a clear target sub-picture from the plurality of sub-pictures; inputting the target sub-picture into a preset picture feature extraction model for feature extraction to obtain a first picture feature vector corresponding to the target sub-picture; based on the first picture feature vector and the position information of the plurality of sub-pictures in the to-be-processed picture, determining second picture feature vectors corresponding to the remaining sub-pictures in the plurality of sub-pictures; and based on the first picture feature vector and the second picture feature vector, determining a third picture feature vector corresponding to the to-be-processed picture. The image feature extraction method is suitable for extracting image features.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to a picture feature extraction method, device, storage medium and computer equipment. Background technique [0002] Image feature extraction is a basic task in natural language processing. Image feature extraction provides the basis for subsequent natural language processing. Therefore, efficient and accurate image feature extraction is of great significance to natural language processing. [0003] At present, a feature extraction model is usually constructed using a clear and complete image, and image feature extraction is performed based on the constructed feature extraction model. However, in actual business scenarios, pictures are often incomplete or unclear, for example, cropped pictures or mosaiced pictures, which will lead to feature extraction models built with clear and complete pictures, which are difficult to support For the feature extraction of unclear or...

AI Technical Summary

Problems solved by technology

However, in actual business scenarios, pictures are often incomplete or unclear, for example, cropped pictures or mosaiced pictures, which will lead to feature extraction models built with clear and complete pictures, which are difficult to support For the feature extraction of unclear or incomplete pictures, the extraction accuracy of picture features cannot be guaranteed

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