ImageCLEF 2014: Overview and Analysis of the Results
By Barbara Caputo, Henning Müller, J. Martínez-Gómez, Mauricio Villegas, B. Acar, Novi Patricia, N. Marvasti, Suzan Üsküdarli, Roberto Paredes Palacios, M. Cazorla and 2 more. 2014
119 citations recorded by the source corpus.
This paper presents an overview of the ImageCLEF 2014 evaluation lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and medical archives. Over the years, by providing new data collections and challenging tasks to the community of interest, the ImageCLEF lab has achieved an unique position in the image annotation and retrieval research landscape. The 2014 edition consists of four tasks: domain adaptation, scalable concept image annotation, liver CT image annotation and robot vision. This paper describes the tasks and the 2014 competition, giving a unifying perspective of the present activities of the lab while discussing future challenges and opportunities.
Subfields: Computer Vision and Pattern Recognition, Molecular Biology, Evaluation & Benchmarks
Citations in the corpus
- Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100 (2020, neutral): “*: Note that Test* refers to ‘Target Test’ Dataset Train Test Classes SD M AAL Year Real/Syn Im a g e Office [105] 4110 N/A 31 1 3 N/A 2010 Real ImageCLEF [106] 2400 600 12 5 1 N/A 2014 Real Office↔ Home [107] 15500 N/A 65 4 1 N/A 2017 Real VisDA-C [108] 280157 N/A 12 3 1 N/A 2017 Real/Syn DomainNet”