The task involves a supervised classification of bird species from a set of bird images.
From an ecological and environmental point of view, monitoring bird diversity is an important task. While bird monitoring is a well-established process, the observation is largely carried out manually which is time-consuming, and hence the scalability is low. This has motivated the use of machine learning methods to analyze bird images and sounds, using camera-trap data, recorder data or crowd-sourcing. In this challenge, we pose the bird image classification task, especially for Himalayan birds, based on a limited but a diverse set of crowd-sourced data. Especially, the present challenge involves a fairly low amount of labelled data, and may require transfer learning based approaches for effective classification.
- A short overview of the existing work on the problem:
There are various approaches developed for bird image classification which mainly involve the Caltech bird image dataset. However, in this challenge we are providing a dataset, which, while smaller has a larger variation in terms of scale, illumination etc.
- Evaluation protocol:
The challenge data distribution will be divided in two phases. First, the training data will be made available, which the participants can use to train and validate their methods. After a few weeks, and closer to the result submission deadline, a test data will be made available. The results will be decided on the F-score metric averaged across classes, which involves True-positives and False-positives.
The data consists of about 500 images, involving 10-13 classes. The precise configuration of the data would be available, when the challenge is available online
- Scheme for evaluating results:
Please see “Evaluation Protocol”
- Timeline of events:
May 25: Challenge website and registration opens
May 30: Training data available
July 20: Registration closes
Aug 1: Test data available
August 31: Submission of results and reports
CVIP 2018: Announcement of the challenge results
- Organization team and contact details:
Arnav Bhavsar (IIT Mandi) (firstname.lastname@example.org) A.D. Dileep (IIT Mandi) (email@example.com) Padmanabhan Rajan (IIT Mandi) (firstname.lastname@example.org)