Deep multi-query video retrieval

Akbacak E., VURAL C.

Journal of Visual Communication and Image Representation, cilt.85, 2022 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 85
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.jvcir.2022.103501
  • Dergi Adı: Journal of Visual Communication and Image Representation
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Communication & Mass Media Index, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC
  • Anahtar Kelimeler: Multi-query video retrieval, Pareto optimization, Video hashing
  • Marmara Üniversitesi Adresli: Evet

Özet

© 2022 Elsevier Inc.Video retrieval methods have been developed for a single query. Multi-query video retrieval problem has not been investigated yet. In this study, an efficient and fast multi-query video retrieval framework is developed. Query videos are assumed to be related to more than one semantic. The framework supports an arbitrary number of video queries. The method is built upon using binary video hash codes. As a result, it is fast and requires a lower storage space. Database and query hash codes are generated by a deep hashing method that not only generates hash codes but also predicts query labels when they are chosen outside the database. The retrieval is based on the Pareto front multi-objective optimization method. Re-ranking performed on the retrieved videos by using non-binary deep features increases the retrieval accuracy considerably. Simulations carried out on two multi-label video databases show that the proposed method is efficient and fast in terms of retrieval accuracy and time.