Quality grading and perceptual hash characteristic combination-based unmanned aerial vehicle image retrieval method

Abstract

The invention discloses a quality grading and perceptual hash characteristic combination-based unmanned aerial vehicle image retrieval method, and belongs to the technical field of image processing. The method comprises the steps of firstly, performing automatic quality grading and quality label allocation on an unmanned aerial vehicle training image set, extracting perceptual hash codes, and establishing database applications corresponding to images and texts; secondly, setting quality label and perceptual hash code-based data attributes for to-be-retrieved images, like the sub-steps in the first step; thirdly, performing Hamming distance matching on the obtained data attributes of the to-be-retrieved images and images in a database; and finally, allocating a certain weight to a similar image set obtained by matching in combination with similarity and quality labels to establish a weight function, performing resorting according to a weight progressive increase sequence, and outputting an image result. According to the method, image characteristics can be quickly extracted and image quality grading can be effectively finished, so that quick and accurate retrieval of the unmanned aerial vehicle images is realized.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a UAV image retrieval method based on a combination of quality classification and perceptual hash features. Background technique [0002] In recent years, unmanned aerial vehicles (UAVs) have played an important role in military, civilian and scientific research fields due to their advantages of low cost, high flexibility and high efficiency. In the military, UAVs can perform tasks such as target detection, identification and tracking, battlefields, communication relays, and air strikes; in civilian use, UAVs occupy an important position in fields such as aerial photography of film and television materials, and emergency handling. ; In terms of scientific research, it can perform tasks such as resource surveying and meteorological observation. UAV images are the basic data obtained by UAV missions, which can provide effective scientific research data for gro...

AI Technical Summary

Problems solved by technology

According to the different retrieval content, content-based image retrieval can be roughly divided into two levels: 1) Based on the underlying visual features, such as color, shape, texture and other features, this level of retrieval is concise and intuitive, relevant There are many studies, and many relatively mature image retrieval algorithms have appeared; 2) Based on semantic content, image retrieval is performed by using high-level semantic descriptions of images. Because it contains high-level concepts, it often requires human knowledge reasoning to perform image retrieval. recognition and interpretation, so this level of retrieval algorithm is relatively complex

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