Overview of the ImageCLEF 2022: Multimedia Retrieval in Medical, Social Media and Nature Applications

• Published in: Experimental IR Meets Multilinguality, Multimodality, and Interaction pp. 541 - 564
• Main Authors: Ionescu, Bogdan, Müller, Henning, Péteri, Renaud, Rückert, Johannes, Abacha, Asma Ben, de Herrera, Alba G. Seco, Friedrich, Christoph M., Bloch, Louise, Brüngel, Raphael, Idrissi-Yaghir, Ahmad, Schäfer, Henning, Kozlovski, Serge, Cid, Yashin Dicente, Kovalev, Vassili, Ştefan, Liviu-Daniel, Constantin, Mihai Gabriel, Dogariu, Mihai, Popescu, Adrian, Deshayes-Chossart, Jérôme, Schindler, Hugo, Chamberlain, Jon, Campello, Antonio, Clark, Adrian
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: coral image segmentation and classification; ImageCLEF lab; late fusion for search diversification and interestingness prediction; medical image caption analysis; Medical image classification; prediction of effects of online image sharing; tuberculosis prediction
• ISBN: 303113642X; 9783031136429
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-13643-6_31

This paper presents an overview of the ImageCLEF 2022 lab that was organized as part of the Conference and Labs of the Evaluation Forum – CLEF Labs 2022. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2022, the 20th edition of ImageCLEF runs four main tasks: (i) a medical task that groups two previous tasks, i.e., caption analysis and tuberculosis prediction, (ii) a social media aware task on estimating potential real-life effects of online image sharing, (iii) a nature coral task about segmenting and labeling collections of coral reef images, and (iv) a new fusion task addressing the design of late fusion schemes for boosting the performance, with two real-world applications: image search diversification (retrieval) and prediction of visual interestingness (regression). The benchmark campaign received the participation of over 25 groups submitting more than 258 runs.