Image retrieval method and device

Abstract

The embodiment of the invention provides an image retrieval method and device, and the method comprises the steps: employing a convolutional neural network to extract the features of a target image, and enabling the total score of pixels in the feature map at the same position to serve as a class activation mapping table of the target image according to the feature map of the target image output by the last convolutional layer in the convolutional neural network; Multiplying each feature map by the class activation mapping table, performing summation pooling, multiplying the summation poolingresult by the weight of each feature map of the target image, and obtaining intermediate features of each feature map of the target image; Obtaining spatial semantic features of each feature map of the target image according to the target image category probability output by the discrimination layer of the convolutional neural network and the intermediate features of each feature map of the targetimage; And obtaining a retrieval result according to the spatial semantic features of the feature maps of the target image and the pre-obtained spatial semantic features of the feature maps of the to-be-retrieved images. According to the embodiment of the invention, the spatial semantic information is used for retrieval, so that the retrieval precision is improved.

Description

technical field [0001] Embodiments of the present invention belong to the technical field of information retrieval, and more specifically, relate to an image retrieval method and device. Background technique [0002] Image retrieval refers to retrieving images similar to a specified image from an image database. Including text-based image retrieval and content-based image retrieval. Text-based image retrieval uses text description to describe the characteristics of images, while content-based image retrieval uses image content semantics, such as color, texture and layout, for analysis and retrieval. [0003] Traditional content-based image retrieval mainly uses handcrafted features, that is, encodes images with mathematical information such as mean and variance to obtain features, and then performs cosine distance or Euclidean distance matching for retrieval. With the achievements of convolutional neural network in image recognition, it is derived to extract the features o...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the problem of inaccurate retrieval results of the above-mentioned existing image retrieval methods or at l

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