Deep learning-based sketch retrieval method

Abstract

The invention discloses a deep learning-based sketch retrieval method, and relates to the technical field of multimedia information retrieval. The method comprises the steps of firstly calculating a conventional picture edge probability graph and obtaining a feature descriptor of the edge probability graph to realize conversion from a color conventional graph to a similar freehand image; establishing a feature library required for freehand image retrieval through a convolutional neural network; and extracting depth features of edge graphs of different levels of the similar freehand image and adrawn sketch by utilizing a deep learning technology to perform similarity matching. By providing new feature extraction and matching methods, the sketch drawn by a user can be understood more accurately; the method has high accuracy and high adaptability; the influence of fuzziness of the freehand sketch can be reduced; the retrieval correlation is improved; the user experience is enhanced; andthe method has wide application values in the field of multimedia image retrieval.

Description

technical field [0001] The invention belongs to the technical field of multimedia information retrieval, and in particular relates to a sketch retrieval method based on deep learning. Background technique [0002] With the popularity of mobile phones, digital cameras, and the development of Internet technology, digital images have exploded in the past few decades. How to effectively search for images has become a hot research object in academia and industry, and many image retrieval systems have emerged as a result. Early image retrieval technologies are mainly divided into two categories according to different input types, the first is Text-based Image Retrieval (TBIR), and the second is Content-based Image Retrieval (Content-based Image Retrieval). , CBIR). [0003] Text-based image retrieval technology refers to the realization of retrieval based on the text entered by the user. This method is more intuitive and accurately reflects the real needs of users. These texts...

AI Technical Summary

Problems solved by technology

[0015] The purpose of the present invention is to provide a sketch retrieval method based on deep learning, by first calculating the edge probability map of the conventional picture and obtaining the feature descriptor of the edge probability map to realize the conversion of the color conventional map to the hand-painted image, and using the convolutional neural network to establish The feature library required for hand-drawn image retrieval, and then according to the deep learning technology to extract the edge maps of different levels of the hand-drawn image and the depth features of the sketch for similarity matching, which solves the problem that the traditional artificially designed features are redundant on hand-drawn drawings. The problem of not robustness and poor affine invariance

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