이미지 검색기를 통한 랜드마크 인식

Landmark recognition through image searcher

  • 김기덕 ((주)쓰리아이퓨처) ;
  • 이근후 ((주)쓰리아이퓨처)
  • Gi-Duk Kim (3IFuture) ;
  • Geun-Hoo Lee (3IFuture)
  • 발행 : 2024.01.17

초록

본 논문에서는 이미지 검색기를 통한 랜드마크 인식 방법을 제안한다. 특정 랜드마크 데이터세트에서 라벨링을 하지 않은 비지도 학습을 통해서 이미지에서 랜드마크의 클래스 분류를 위한 특징을 추출한다. 학습된 모델을 랜드마크 데이터세트인 Paris6k 데이터세트와 Oxford5k 데이터세트에 적용하여 랜드마크 인식 정확도를 확인하였다. 성능과 속도를 강화하기 위해 이미지 특징 추출 모델로 ResNet 대신에 YOLO에서 사용된 CSPDarknet-53을 사용하여 모델의 크기를 줄이고 랜드마크 인식 정확도를 높였다. 그리고 모델로부터 추출된 특징의 수를 줄여 이미지 검색 시 소요되는 시간을 감소시켰다. 학습된 모델로 rOxford5k 데이터 세트에 적용 시 mAP 80.37, rParis6k에서 mAP 89.07을 얻었다.

키워드

참고문헌

  1. BABENKO, Artem, et al. Neural codes for image retrieval. In: Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13. Springer International Publishing, 2014. p. 584-599. 
  2. ARANDJELOVIC, Relja, et al. NetVLAD: CNN architecture for weakly supervised place recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 5297-5307. 
  3. RADENOVIC, Filip; TOLIAS, Giorgos; CHUM, Ondrej. Fine-tuning CNN image retrieval with no human annotation. IEEE transactions on pattern analysis and machine intelligence, 2018, 41.7: 1655-1668. 
  4. PHILBIN, James, et al. Object retrieval with large vocabularies and fast spatial matching. In: 2007 IEEE conference on computer vision and pattern recognition. IEEE, 2007. p. 1-8. 
  5. PHILBIN, James, et al. Lost in quantization: Improving particular object retrieval in large scale image databases. In: 2008 IEEE conference on computer vision and pattern recognition. IEEE, 2008. p. 1-8. 
  6. HE, Kaiming, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. p. 770-778. 
  7. REDMON, Joseph; FARHADI, Ali. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. 
  8. PERRONNIN, Florent, et al. Large-scale image retrieval with compressed fisher vectors. In: 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE, 2010. p. 3384-3391. 
  9. JEGOU, Herve, et al. Aggregating local descriptors into a compact image representation. In: 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE, 2010. p. 3304-3311. 
  10. PHILBIN, James, et al. Object retrieval with large vocabularies and fast spatial matching. In: 2007 IEEE conference on computer vision and pattern recognition. IEEE, 2007. p. 1-8. 
  11. SHEN, Xiaohui, et al. Spatially-constrained similarity measurefor large-scale object retrieval. IEEE transactions on pattern analysis and machine intelligence, 2013, 36.6: 1229-1241. 
  12. CHUM, Ondrej, et al. Total recall II: Query expansion revisited. In: CVPR 2011. IEEE, 2011. p. 889-896. 
  13. CHOPRA, Sumit; HADSELL, Raia; LECUN, Yann. Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05). IEEE, 2005. p. 539-546. 
  14. WANG, Jiang, et al. Learning fine-grained image similarity with deep ranking. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. p. 1386-1393.