A novel multilabel video retrieval method using multiple video queries and deep hash codes
Enver Akbacak · Concurrency and Computation Practice and Experience · 2022
Abstract Most videos consist of multiple clips. Namely, videos usually have multiple spatio‐temporal information. However, existing video datasets are of single video clips. Additionally, content‐based video retrieval methods have been implemented based on a single query clip. However, a single query may not be sufficient for better expressing intention concerning the query. Besides, it narrows the capability to search for multiple semantics. How to deal with retrieving videos having multiple clips has not been adequately investigated. This study proposes a novel multilabel video retrieval method that utilizes multiple video clips as queries. Whatever the number of queries, it transforms a multi‐query video retrieval into a single‐query video retrieval. The method is independent of both the video features and distance metric types. However, we propose a deep video hashing method to achieve speed and efficiency. Experiments performed on three prepared multilabel video datasets have proven the effectiveness of the proposed method. The source codes of the study are public.