1. ELPephants: A Fine-Grained Dataset for Elephant Re-Identification
- Author
-
Joachim Denzler and Matthias Körschens
- Subjects
Computer science ,business.industry ,0102 computer and information sciences ,02 engineering and technology ,Machine learning ,computer.software_genre ,01 natural sciences ,Re identification ,Image (mathematics) ,Set (abstract data type) ,Support vector machine ,010201 computation theory & mathematics ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Baseline (configuration management) ,business ,computer - Abstract
Despite many possible applications, machine learning and computer vision approaches are very rarely utilized in biodiversity monitoring. One reason for this might be that automatic image analysis in biodiversity research often poses a unique set of challenges, some of which are not commonly found in many popular datasets. Thus, suitable image datasets are necessary for the development of appropriate algorithms tackling these challenges. In this paper we introduce the ELPephants dataset, a re-identification dataset, which contains 276 elephant individuals in 2078 images following a long-tailed distribution. It offers many different challenges, like fine-grained differences between the individuals, inferring a new view on the elephant from only one training side, aging effects on the animals and large differences in skin color. We also present a baseline approach, which is a system using a YOLO object detector, feature extraction of ImageNet features and discrimination using a support vector machine. This system achieves a top-1 accuracy of 56% and top-10 accuracy of 80% on the ELPephants dataset.
- Published
- 2019