The Placing Task: A Large-Scale Geo-Estimation Challenge for Social-Media Videos and Images

By Jaeyoung Choi, Bart Thomee, Gerald Friedland, Liangliang Cao, Karl S. Ni, Damian Borth, Benjamin Elizalde, L. Gottlieb, C. Carrano, Roger A. Pearce and 1 more. 2014

41 citations recorded by the source corpus.

The Placing Task is a yearly challenge offered by the MediaEval Multimedia Benchmarking Initiative that requires participants to develop algorithms that automatically predict the geo-location of social media videos and images. We introduce a recent development of a new standardized web-scale geo-tagged dataset for Placing Task 2014, which contains 5.5 million photos and 35,000 videos. This standardized benchmark with a large persistent dataset allows research community to easily evaluate new algorithms and to analyze their performance with respect to the state-of-the-art approaches. We discuss the characteristics of this year's Placing Task along with the description of the new dataset components and how they were collected.

Subfields: Computer Vision and Pattern Recognition, Remote Sensing

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In Computer Vision and Pattern Recognition

Citations in the corpus

YFCC100M (2015, neutral): “As an example, the organizers of the MediaEval Placing Task made, in addition to the training and test sets, the visual and aural features they extracted from the content available for download.”
Large-Scale Deep Learning on the YFCC100M Dataset (2015, methodology): “In fact, the 2014 MediaEval Placing Task is using YFCC100M as the source of benchmark data [15].”
Real-time Analysis and Visualization of the YFCC100m Dataset (2015, background): “However, often researchers are interested in a particular subset of a dataset like for example the events in [1] or images and videos with geo-location information [4].”
The New Data and New Challenges in Multimedia Research (2015, unknown): “For example, the MediaEval Placing Task [27], an annual benchmark in which participants develop algorithms for estimating the geographic location where a photo or video was taken, is currently based on our dataset.”
The Placing Task at MediaEval 2016 (2016, methodology): “…used in multimedia analysis for each of the media objects including SIFT, Gist, color and texture histograms for visual analysis, and MFCC for audio analysis [3], which together with the original photo and video content are publicly and freely available through the Multimedia Commons Initiative 4 .”
The YLI-MED Corpus: Characteristics, Procedures, and Plans (2015, methodology): “Details of the YLI feature-computation procedures and technical basis can be found in Ni et al. 2014 and Choi et al. 2014.”