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The ability to visually re-identify objects is a fundamental capability invision systems. Oftentimes, it relies on collections of visual signatures basedon descriptors, such as SIFT or SURF. However, these traditional descriptorswere designed for a certain domain of surface appearances and geometries(limited relief). Consequently, highly-textured surfaces such as tree bark posea challenge to them. In turn, this makes it more difficult to use trees asidentifiable landmarks for navigational purposes (robotics) or to track felledlumber along a supply chain (logistics). We thus propose to use data-drivendescriptors trained on bark images for tree surface re-identification. To thiseffect, we collected a large dataset containing 2,400 bark images with strongillumination changes, annotated by surface and with the ability to pixel-alignthem. We used this dataset to sample from more than 2 million 64x64 pixelpatches to train our novel local descriptors DeepBark and SqueezeBark. OurDeepBark method has shown a clear advantage against the hand-crafteddescriptors SIFT and SURF. For instance, we demonstrated that DeepBark canreach a mAP of 87.2% when retrieving 11 relevant bark images, i.e.corresponding to the same physical surface, to a bark query against 7,900images. Our work thus suggests that re-identifying tree surfaces in achallenging illuminations context is possible. We also make public our dataset,which can be used to benchmark surface re-identification techniques.

Tree bark re-identification using a deep-learning feature descriptor