Unsupervised Distance Learning for Plant Species Identification

Abstract

Phenology is among the most trustworthy indicators of climate change effects on plants and animals. The recent application of repeated digital photographs to monitor vegetation phenology has provided accurate measures of plant life cycle changes over time. A fundamental requirement for phenology studies refers to the correct recognition of phenological patterns from plants by taking into account time series associated with their crowns. This paper presents a new similarity measure for identifying plants based on the use of an unsupervised distance learning scheme, instead of using traditional approaches based on pairwise similarities. We experimentally show that its use yields considerable improvements in time-series search tasks. In addition, we also demonstrate how the late fusion of different time series can improve the results on plant species identification. In some cases, significant gains were observed (up to +8.21% and +19.39% for mean average precision and precision at 10 scores, respectively) when compared with the use of time series in isolation.