Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset

Zinnen M, Madhu P, Leemans I, Bell P, Hussian A, Tran TMH, Hürriyetoğlu A, Maier A, Christlein V (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Expert Systems With Applications Elsevier

Book Volume: 255, Part B

Article Number: 124576

DOI: 10.1016/j.eswa.2024.124576

Abstract

Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. Existing datasets provide instance-level annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The proposed ODOR dataset fills this gap, offering 38,116 object-level annotations across 4712 images, spanning an extensive set of 139 fine-grained categories. Conducting a statistical analysis, we showcase challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. Furthermore, we provide an extensive baseline analysis for object detection models and highlight the challenging properties of the dataset through a set of secondary studies. Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.

Authors with CRIS profile

Mathias Zinnen Lehrstuhl für Informatik 5 (Mustererkennung) Prathmesh Madhu Lehrstuhl für Informatik 5 (Mustererkennung) Azhar Hussian Lehrstuhl für Informatik 5 (Mustererkennung) Thi My Hang Tran Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Vincent Christlein Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Philipps-Universität Marburg DE Germany (DE) Royal Netherlands Academy of Arts and Sciences / Koninklijke Nederlandse Akademie van Wetenschappen (KNAW) NL Netherlands (NL)

How to cite

APA:

Zinnen, M., Madhu, P., Leemans, I., Bell, P., Hussian, A., Tran, T.M.H.,... Christlein, V. (2024). Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset. Expert Systems With Applications, 255, Part B. https://doi.org/10.1016/j.eswa.2024.124576

MLA:

Zinnen, Mathias, et al. "Smelly, dense, and spreaded: The Object Detection for Olfactory References (ODOR) dataset." Expert Systems With Applications 255, Part B (2024).

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