Efficient text-based query based on multi-level and deep-semantic multimedia indexing and retrieval

Recent technological advancements have led to a significant increase in the quantity and accessibility of videos. The decrease in video acquisition costs and the increase in memory capacity have made it possible to store large video collections in computer systems. To effectively exploit these collections, it is crucial to have tools that facilitate access and management. In this paper, we present a multimedia retrieval approach that prioritizes the user's needs by starting with a text-based query. The approach consists of two main parts: (i) a new multi-level and deep-semantic video classification indexing method, and (ii) a query expansion mechanism and relevance feedback system to improve the results based on the user's feedback. Our contribution is demonstrated through the implementation of the Deep-VISEN prototype and experiments on a collection of 2700 videos and 62838 images. The results show that our algorithm is effective and precise.