RoI detection and segmentation algorithms for marine mammals photo-identification

Abstract

Traditional marine mammal photo-identification is based on recognizing the appearances of the same individuals in pictures taken at different places and times. This task is traditionally performed by Biologists or other Scientists, which may demand a heavy cognitive burden and appreciable processing time searching and selecting information from thousands of pictures. Recently crowdsourcing and citizen science arose as a significant information source of potential scientific use. In particular, the use of non-professional photographs taken by the general public is being leveraged by many scientific projects. This represents an opportunity to enlarge the picture database required in trustable capture-recapture models, but at the same time human-assisted matching becomes unfeasible. Automated image analysis may represent an obvious aid, but applying image analytics to match individuals in large unfiltered datasets may be too slow and full of spurious results. Another strategy may be first to filter out useless images or parts thereof, retaining only the regions of interest (RoIs) in which appears the actual visible portion of the animal to be identified. In this work, we explore and develop a multi-criterion RoI detection for marine mammal pictures taken in the open. Particularly we focus on Commerson's dolphins pictures. Popular RoI detection algorithms, like Haar-wavelet-based methods, are show to perform poorly. For this reason, a convolutional neural network and a multifractal classifier based on color and texture features were developed, achieving significantly better outcomes. The resulting RoIs are much more robust, can be automated, and reduce the further burden of the identification process, either assisted or unassisted.