KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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Sentence Recommendation Using Beam Search in a Military Intelligent Image Analysis System

군사용 지능형 영상 판독 시스템에서의 빔서치를 활용한 문장 추천

  • Received : 2021.10.07
  • Accepted : 2021.10.23
  • Published : 2021.11.30
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Abstract

Existing image analysis systems in use in the military field are carried out by readers analyzing and identifying images themselves, writing and disseminating related content, and in this process, repetitive tasks are frequent, resulting in workload. In this paper, to solve the previous problem, we proposed an algorithm that can operate the Seq2Seq model on a word basis, which operates on a sentence basis, and applied the Attention technique to improve accuracy. In addition, by applying the Beam Search technique, we would like to recommend various current identification sentences based on the past identification contents of a specific area. It was confirmed through experiments that the Beam Search technique recommends sentences more effectively than the existing greedy Search technique, and confirmed that the accuracy of recommendation increases when the size of Beam is large.

군사 분야에서 사용 중인 기존 영상 판독 시스템은 판독관들이 직접 영상을 분석 및 식별하여 관련 내용을 보고서에 작성하고 전파하는 방식으로 진행되는데 이 과정에서 반복 작업이 빈번하여 업무 과부하가 발생한다. 본 논문에서는 이러한 문제를 해결하고자, 기존의 문장 단위로 동작하는 Seq2Seq 모델을 단어 단위로 동작할 수 있는 알고리즘을 제안하고, Attention 기법을 적용해 정확도를 향상시키고자 한다. 또한 Beam 탐색 기법을 응용하여 특정 지역의 과거 식별내용을 바탕으로 현재 식별 문장을 다양하게 추천하고자 한다. 실험을 통해 Beam 탐색 기법이 기존 Greedy 탐색 기법보다 효과적으로 문장을 추천하는 것을 확인하였고, Beam의 크기가 클 때 추천의 정확도가 높아지는 것을 확인하였다.

Keywords

Acknowledgement

이 논문은 국방과학연구소의 지원을 받아 수행된 연구임(UD190025FD).

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