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UAV Image Classification and Recognition Based on SVM and Non-Reference Adaptive Quality Evaluation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

I136 classification and recognition are the hotspots of UAV image processing and have broad application prospects. However it also has the disadvantage of heavy load and low efficiency to operators. In this paper, images are effectively processed as classification, layered and grade with a new type of algorithm combining with sparse matrix and SVM and feature extraction. Sparse matrix is used to classify images. And image quality features of wavelet transform, DCT, HAAR and entropy are extracted under image preprocessing to be normalized. Then, trainers and testers are constructed with cross-validated and multi-kernel parameters optimization under no reference. And the feedback of optionally retest result under full reference or semi-reference quality is modifying the trainer. Finally a new model with combing confused matrix is built to achieve adaptive quality evaluation.

Original languageEnglish
Title of host publicationProceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018
EditorsBing Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages407-410
Number of pages4
ISBN (Electronic)9781538618035
DOIs
StatePublished - 20 Sep 2018
Event2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018 - Xi'an, China
Duration: 25 May 201827 May 2018

Publication series

NameProceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

Conference

Conference2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018
Country/TerritoryChina
CityXi'an
Period25/05/1827/05/18

Keywords

  • SVM
  • adaptive quality evaluation
  • image feature
  • multi-kernel parameters optimization

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