Deep learning-based freehand sketch image retrieval method

Abstract

The invention belongs to the technical field of multimedia information retrieval, and specifically discloses a deep learning-based freehand sketch image retrieval method. According to the method, and edge contour detection technology and a non-maximum value suppression technology are utilized to realize the conversion from colored images to similar sketch images, a deep learning technology is utilized to construct distinguishing feature expressions for querying deep features of sketches and similar sketches, the deep features fuse the high-level semantic features and low-level visual features of images, and the deep features are more distinguishing in sketch retrieval. Through deeply mining visual information of a first retrieval result, uncorrelated images placed at the front in the retrieval result are rejected and a more correlated result is returned to the users. The method is high in correctness and strong in adaptability. On the basis of large-scale image data, the method is significant in carrying out efficient image retrieval which considers semantic information of sketches, so that the influences of fuzziness of freehand sketches can be decreased, the retrieval correlation can be improved and the user experience can be enhanced; and the method has an extensive application value 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 hand-drawn sketch image retrieval method based on deep learning. Background technique [0002] With the popularity of image acquisition devices such as mobile phones and digital cameras and the development of Internet technology, digital images have exploded in the past few decades. Some image sharing websites, such as Flickr, upload millions of images every day. 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 retrie...

AI Technical Summary

Problems solved by technology

[0019] (2) Fully mine the multi-level information of sketches - most existing sketch retrieval techniques only consider the visual information of sketches, ignoring the high-level semantic information of sketches

However, in most retrievals, the initial ranking results are often accompanied by images that are not relevant to the query, and they sometimes occupy a higher ranking position, which greatly affects the user experience

The simple matching method cannot avoid the above problems

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