Image retrieval method based on deep convolution characteristic and semantic similarity measurement

Abstract

The invention relates to an image retrieval method based on deep convolution characteristic and semantic similarity measurement, and belongs to the correlation fields such as computer vision, image processing and image understanding. Firstly, aiming at an image set, convolution layer characteristics of each image are extracted through a trained deep convolution neutral network mode, the extractedconvolution layer characteristics are subjected to polymerization representation, the convolution layer characteristics are subjected to semantic description through an AFS framework, an image similarity measuring method based on semantic similarity is defined on the basis, the similarity of images in an image library are calculated, and finally the similarity is ranked to complete an image retrieval task. The problems that a traditional retrieval method based on bottom layer visual characteristics currently is in lack of semantic and low in accuracy can be effectively solved, and the actual requirements of users on image retrieval based on content are better met.

Description

technical field [0001] The invention relates to an image retrieval method based on deep convolution features and semantic similarity measurement, and belongs to the technical field of computer image retrieval. Background technique [0002] Content based image retrieval (Content based Image Retrieval, CBIR) has always been one of the research hotspots in the field of computer vision. With the rapid increase of multimedia information in the Internet age, how to quickly and accurately retrieve images that meet user requirements from massive image data covering various contents is a very challenging task. In CBIR, image feature extraction and image similarity measurement are two key links. [0003] In recent years, with the successful application of deep learning technology in the field of image recognition, convolutional neural networks (CNNs) have been used as a feature extraction method to obtain deep convolutional features with high-level semantics, so as to improve the acc...

AI Technical Summary

Problems solved by technology

Obviously, this method only considers the similarity relationship between two images and ignores the similarity structure inside the image, which cannot accurately and comprehensively describe the true similarity between images.

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