Co-segmentation methods for wildlife-photo identification

Abstract

Co-segmentation is defined as the task of jointly segmenting shared objects in a given set of images. This work is concentrated on the case when the segmented object is an animal, which means that the segmentation might require additional information about animal biometrics. The aim of this thesis was to survey the existing segmentation and co-segmentation methods with respect to the task of wildlife photo-identification, to overview existing datasets for co-segmentation and to evaluate and compare existing co-segmentation algorithms. In this study four co-segmentation algorithms were compared: Discriminative clustering, Multiple foreground co-segmentation, Multiple random walkers, and Distributed co-segmentation via submodular optimization. The comparison was performed using various datasets with wildlife animals. In most cases the Multiple random walkers method showed the best results.