Results 11 to 20 of about 752,812 (225)

The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

IEEE Access, 2019
Web contains a vast amount of data, which are accumulated, studied, and utilized by a huge number of users on a daily basis. A substantial amount of data on the Web is available in an unstructured format, such as Web pages, books, journals, and files ...
Muhammad Nabeel Asim, Muhammad Wasim, Muhammad Usman Ghani Khan, Nasir Mahmood, Waqar Mahmood   +4 more
doaj   +2 more sources

Adaptive Confidence Multi-View Hashing for Multimedia Retrieval [PDF]

IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023
The multi-view hash method converts heterogeneous data from multiple views into binary hash codes, which is one of the critical technologies in multimedia retrieval.
Jian Zhu, Yu Cui, Zhangmin Huang, Xingyu Li, Lei Liu, Lingfang Zeng, Linzhi Dai   +6 more
semanticscholar   +1 more source

Deep Metric Multi-View Hashing for Multimedia Retrieval [PDF]

IEEE International Conference on Multimedia and Expo, 2023
Learning the hash representation of multi-view heterogeneous data is an important task in multimedia retrieval. However, existing methods fail to effectively fuse the multi-view features and utilize the metric information provided by the dissimilar ...
Jian Zhu, Zhangmin Huang, Xiaohui Ruan, Yunbo Cui, Yongli Cheng, Lingfang Zeng   +5 more
semanticscholar   +1 more source

ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Internet Applications [PDF]

Advances in Information Retrieval, 2020
This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum—CLEF Labs 2020 in Thessaloniki, Greece.
Ionescu B, Müller H, Péteri R, Dang-Nguyen D, Zhou L, Piras L, Riegler M, Halvorsen P, Tran M, Lux M, Gurrin C, Chamberlain J, Clark A, Campello A, Seco de Herrera A, Ben Abacha A, Datla V, Hasan S, Liu J, Demner-Fushman D, Pelka O, Friedrich C, Dicente Cid Y, Kozlovski S, Liauchuk V, Kovalev V, Berari R, Brie P, Fichou D, Dogariu M, Stefan L, Constantin M.   +31 more
europepmc   +2 more sources

Intelligent design of network multimedia using big data and virtual Artificial Intelligence technology

IET Networks, EarlyView., 2023
The topic discussed was the intelligent design of network multimedia using BD and virtual AI technology. First of all, the authors gave a brief overview of its relevant research background, and then comprehensively analysed the advantages and disadvantages of previous scholars' research on network multimedia.
Xin Zhang
wiley   +1 more source

A new design of multimedia big data retrieval enabled by deep feature learning and Adaptive Semantic Similarity Function

Multimedia Systems, 2022
Nowadays, multimedia big data have grown exponentially in diverse applications like social networks, transportation, health, and e-commerce, etc. Accessing preferred data in large-scale datasets needs efficient and sophisticated retrieval approaches ...
D. Sujatha, ·. M. Subramaniam, Chinnanadar Ramachandran, Rene Robin   +3 more
semanticscholar   +1 more source

Performance Evaluation in Multimedia Retrieval [PDF]

ACM Trans. Multim. Comput. Commun. Appl.
Performance evaluation in multimedia retrieval, as in the information retrieval domain at large, relies heavily on retrieval experiments, employing a broad range of techniques and metrics. These can involve human-in-the-loop and machine-only settings for
Loris Sauter, Ralph Gasser, Heiko Schuldt, Abraham Bernstein, Luca Rossetto   +4 more
semanticscholar   +1 more source

Introducing Multimedia Information Retrieval to libraries

JLIS.it, 2016
The paper aims to introduce libraries to the view that operating within the terms of traditional Information Retrieval (IR), only through textual language, is limitative, and that considering broader criteria, as those of Multimedia Information ...
Roberto Raieli
doaj   +9 more sources

Cottontail DB: An Open Source Database System for Multimedia Retrieval and Analysis

ACM Multimedia, 2020
Multimedia retrieval and analysis are two important areas in "Big data" research. They have in common that they work with feature vectors as proxies for the media objects themselves.
Ralph Gasser, Luca Rossetto, Silvan Heller, H. Schuldt   +3 more
semanticscholar   +1 more source

Content-Based Multimedia Retrieval [PDF]

, 2015
Content-based multimedia information retrieval (IR) provides new models and methods for effectively and efficiently “searching” through the huge variety of media that are available in different kinds of repositories (digital libraries, Web portals, social networks, multimedia databases, etc.).
Flora Amato, Luca Greco, Fabio Persia, Silvestro Roberto Poccia, Aniello De Santo   +4 more
openaire   +2 more sources