Comparing Class-Aware and Pairwise Loss Functions for Deep Metric Learning in Wildlife Re-Identification

Abstract

Similarity learning using deep convolutional neural networks has been applied extensively in solving computer vision problems. This attraction is supported by its success in one-shot and zero-shot classification applications. The advances in similarity learning are essential for smaller datasets or datasets in which few class labels exist per class such as wildlife re-identification. Improving the performance of similarity learning models comes with developing new sampling techniques and designing loss functions better suited to training similarity in neural networks. However, the impact of these advances is tested on larger datasets, with limited attention given to smaller imbalanced datasets such as those found in unique wildlife re-identification. To this end, we test the advances in loss functions for similarity learning on several animal re-identification tasks. We add two new public datasets, Nyala and Lions, to the challenge of animal re-identification. Our results are state of the art on all public datasets tested except Pandas. The achieved Top-1 Recall is 94.8% on the Zebra dataset, 72.3% on the Nyala dataset, 79.7% on the Chimps dataset and, on the Tiger dataset, it is 88.9%. For the Lion dataset, we set a new benchmark at 94.8%. We find that the best performing loss function across all datasets is generally the triplet loss; however, there is only a marginal improvement compared to the performance achieved by Proxy-NCA models. We demonstrate that no single neural network architecture combined with a loss function is best suited for all datasets, although VGG-11 may be the most robust first choice. Our results highlight the need for broader experimentation and exploration of loss functions and neural network architecture for the more challenging task, over classical benchmarks, of wildlife re-identification.

An open‐source general purpose machine learning framework for individual animal re‐identification using few‐shot learning

“…For the publicly available datasets, randomized time‐unaware splitting (see Figure 5) was performed in order to make the results described herein comparable with the existing literature (Dlamini & van Zyl, 2021; Schneider et al., 2020). Time‐unaware splitting randomly splits images into the training and test sets using an 80/20 split: 80% of images in the training set, and 20% in the testing set.…”

“…However, this method was only applicable to a single species and required manual processing of each image into a coding system that was still not very reliable with a 23% error rate in the first test. Methodologies for animal re‐identification for multiple different species using similarity learning networks have been previously described (Dlamini & van Zyl, 2021; Miele et al., 2020; Schneider et al., 2022), however, none of these publications made their code publicly available. With an mAP@1 greater than 99% in multiple datasets with different species of sea stars and over 83% in all other species, the methodology described herein advances the field of animal re‐identification by improving state‐of‐the‐art results and providing open‐source code that can help reproduce these results and accelerate the creation of applications using this technology.…”

“…To compare our methodology with existing results, performance against existing, publicly available datasets was evaluated using randomized, time-unaware splits and the open-set evaluation metric as described in previous research (Dlamini & van Zyl, 2021). Using the open-set evaluation metric, the model is evaluated using only previously unseen individuals and the mAP@N is calculated based on whether one of the nearest N neighbours belong to the same individual.…”

“…Amur Tiger Re-identification in the Wild (S. Li et al, 2019) 1887 Images (92 individuals) 0.9974 0.889 (Dlamini & van Zyl, 2021) Beef Cattle Muzzle/Noseprint Database (Xiong et al, 2021) 4923 Images (268 individuals) 0.9937 0.987 (Xiong et al, 2021) Chimpanzee Faces (Freytag et al, 2016) 6770 Images (78 individuals) 0.8385 0.797 (Dlamini & van Zyl, 2021) Great Zebra and Giraffe Count and ID (Parham et al, 2017) 4948 These results represent an improvement in the accuracy of previously published animal re-identification work, particularly for sea stars (Glynn, 1982). A photo recognition program and code has been developed to identify individual knobby stars (Protoreaster nodosus) by coloration and tubercles (Chim & Tan, 2012).…”

“…described herein comparable with the existing literature(Dlamini & van Zyl, 2021;Schneider et al, 2020). Time-unaware splitting ran-domly splits images into the training and test sets using an 80/20 F I G U R E 3 Sample images for the ANAU dataset from a single Anthenea australis sea star.…”