Multimodal Video Indexing: A Review of the State-of-the-art

By Cees G. M. Snoek, Marcel Worring. Multimedia tools and applications, 2004

574 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1983440d65aa0c62153a14950930bde225652113

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Computing (2021, background): “The assessment of human emotion within built environment is nascent and must garner greater study to understand how machine learning can be used to facilitate our understanding of human– environment interactions.”
A new approach to cross-modal multimedia retrieval (2010, background): “In parallel, advances have been reported in the area of multi-modal retrieval systems [21, 29, 26, 28, 6].”
Concept-Based Video Retrieval (2009, result): “In contrast to other reviews on video retrieval, which emphasize either content-based analysis [221], machine learning [158], text and image retrieval [283], search strategies [118], interactive browsing Fig.”
Multimodal fusion for multimedia analysis: a survey (2010, background): “Existing surveys in this direction are mostly focused on a particular aspect of the analysis task, such as multimodal video indexing [26, 120]; automatic audio-visual speech recognition [106]; biometric audiovisual speech synchrony [20]; multi-sensor management for information fusion [146]; face rec”
Focus: Querying Large Video Datasets with Low Latency and Low Cost (2018, background): “A large body of works in multimedia and information retrieval research propose various content-based video indexing and retrieval techniques to facilitate queries on videos (e.g., [40,48,68,69]).”
Video suggestion and discovery for youtube: taking random walks through the view graph (2008, background): “H.3.3 [Information Systems]: Information Storage and Retrieval—Information Search and Retrieval”
Learning to Separate Object Sounds by Watching Unlabeled Video (2018, background): “Despite a long history of audio-visual video indexing [45,57,70,71,79], objects in video are often analyzed as if they were silent entities in silent environments.”
On the Role of Correlation and Abstraction in Cross-Modal Multimedia Retrieval (2014, background): “The first is that low-level cross-modal correlations should be accounted for.”
Parallel field alignment for cross media retrieval (2013, background): “To address the cross media retrieval problem, advances have been reported over the last decades [7, 26, 28].”
I2T: Image Parsing to Text Description (2010, background): “A number of domain-specific CBIR systems have achieved success (see surveys [6], [7], [8], [9], [10]), but these CBIR systems cannot compete with human visual system in terms of robustness, flexibility and shear number of object categories recognizable.”
Information Retrieval Evaluation (2011, methodology): “For more on video retrieval, see both the TRECvid site and references from the video retrieval community such as [160].”
Image retrieval: Ideas, influences, and trends of the new age (2008, background): “Comprehensive surveys exist on the topic of CBIR [Aigrain et al. 1996; Rui et al. 1999; Smeulders et al. 2000; Snoek and Worring 2005], all of which deal primarily with work prior to the year 2000.”
Multimodal Machine Learning: A Survey and Taxonomy (2017, background): “While earlier approaches for indexing and searching these multimedia videos were keyword-based [188], new research problems emerged when trying to search the visual and multimodal content directly.”
Up next: retrieval methods for large scale related video suggestion (2014, methodology): “There is a large variety of methods described in the scien-tiﬁc literature (e.g., [16, 27, 28, 29, 30] among many others) on how to derive a set of annotations for images and videos.”
Cross-Modality Submodular Dictionary Learning for Information Retrieval (2014, background): “…representations Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.”