Concept-Based Video Retrieval

By Cees G. M. Snoek, Marcel Worring. Foundations and Trends in Information Retrieval, 2009

450 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/987dd3dd6079e5fa8a10a1c53b2580fd71e27ede

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In Computer Vision and Pattern Recognition

Citations in the corpus

A Survey on Visual Content-Based Video Indexing and Retrieval (2011, background): “Snoek and Worring [262] present a detailed review of concept-based video retrieval.”
High-level event recognition in unconstrained videos (2012, background): “In [130], Snoek and Worring surveyed approaches to mul-”
Latent Hierarchical Model of Temporal Structure for Complex Activity Classification (2014, background): “It has wide applications in video surveillance [4], [5], human-computer interface [6], sports video analysis [7], and content based video retrieval [8].”
Attribute Learning for Understanding Unstructured Social Activity (2012, background): “User-defined video attribute learning is related to the video concept detection (video ontology) work in the multimedia community [16,17,18,19,20,21,22,23] which has defined top-down shared visual concepts, in order to recognise them in video.”
Aligning plot synopses to videos for story-based retrieval (2015, background): “Two related ones, multimedia event detection (MED) and semantic indexing (SIN) focus on concept detection [40] and work primarily in the domain of unstructured user-generated videos.”
Towards textually describing complex video contents with audio-visual concept classifiers (2011, background): “In each of the aforementioned works on content search [1, 9].”
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]).”
Visual Semantic Search: Retrieving Videos via Complex Textual Queries (2014, methodology): “All objects that were annotated in this way and are part of KITTI classes (cars, vans, trucks, pedestrians, cyclists) were then matched to KITTI ground-truth, which provided us with 3D GT trajectories across each video segment.”
Large-scale visual sentiment ontology and detectors using adjective noun pairs (2013, background): “The challenge of automatically detecting semantic concepts such as objects, locations, and activities in visual data, referred to as video annotation [1], concept detection [28], semantic indexing [25] or multimedia event detection [20], has been studied extensively over the last decade.”
Content-based visual search learned from social media (2012, methodology): “Promotor: Arnold W.M. Smeulders; Co-Promotors: Marcel Worring, Cees G.M. Snoek; Opponents: James Z. Wang, Maarten de Rijke, Guus Schreiber, Alan Hanjalic ISBN number: 9789461820822 http://dare.uva.nl/en/record/410580 Intelligent Systems Lab Amsterdam http://isla.science.uva.nl/ The Intelligent Sy”