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Multimodal query‐level fusion for efficient multimedia information retrieval
Author(s) -
Sattari Saeid,
Yazici Adnan
Publication year - 2018
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21920
Subject(s) - computer science , information retrieval , query expansion , query language , modalities , query optimization , multimedia information retrieval , video retrieval , sargable , web query classification , web search query , data mining , search engine , social science , sociology
Managing a large volume of multimedia data containing various modalities such as visual, audio, and text reveals the necessity for efficient methods for modeling, processing, storing, and retrieving complex data. In this paper, we propose a fusion‐based approach at the query level to improve query retrieval performance of multimedia data. We discuss various flexible query types including the combination of content as well as concept‐based queries that provide users with the ability to efficiently perform multimodal querying. We have carried out a number of experiments on a video database to show the efficiency of our approach for various types of queries. Our experimental results show that our query‐level fusion approach presents a notable improvement in retrieval performance especially for the concept‐based queries.