scholarly journals Revisiting giraffe photo-identification using deep learning and network analysis

2020 ◽  
Author(s):  
Vincent Miele ◽  
Gaspard Dussert ◽  
Bruno Spataro ◽  
Simon Chamaillé-Jammes ◽  
Dominique Allainé ◽  
...  
Keyword(s):  
Metric Learning ◽  
Simple Task ◽  
Photo Identification ◽  
Software Packages ◽  
Animal Populations ◽  
In The Wild ◽  
Capture Recapture ◽  
Deep Metric Learning ◽  
Similarity Networks ◽  
Do So

AbstractAn increasing number of research programs rely on photographic capture-recapture (vs. direct marking) of individuals to study distribution and demography within animal populations. Photo-identification of individuals living in the wild is sometimes feasible using idiosyncratic coat or skin patterns, like for giraffes. When performed manually, the task is tedious and becomes almost impossible as populations grow in size. Computer vision techniques are an appealing and unavoidable help to tackle this apparently simple task in the big-data era. In this context, we propose to revisit giraffe re-identification using convolutional neural networks (CNNs).We first developed an end-to-end pipeline to retrieve a comprehensive set of re-identified giraffes from about 4, 000 raw photographs. To do so, we combined CNN-based object detection, SIFT pattern matching, and image similarity networks. We then quantified the performance of deep metric learning to retrieve the identity of known and unknown individuals. The re-identification performance of CNNs reached a top 5 accuracy of about 90%. Fully based on open-source software packages, our work paves the way for further attempts to build CNN-based pipelines for re-identification of individual animals, in giraffes but also in other species.

scholarly journals An approach to partial occlusion using deep metric learning

2021 ◽  
Vol 10 (3) ◽  
pp. 204
Author(s):  
Chethana Hadya Thammaiah ◽  
Trisiladevi Chandrakant Nagavi
Keyword(s):  
Euclidean Distance ◽  
Metric Learning ◽  
Partial Occlusion ◽  
System A ◽  
Learning Tasks ◽  
Optimal Distance ◽  
Biometric Systems ◽  
In The Wild ◽  
Deep Metric Learning ◽  
Histogram Of Gradients

<span>The human face can be used as an identification and authentication tool in biometric systems. Face recognition in forensics is a challenging task due to the presence of partial occlusion features like wearing a hat, sunglasses, scarf, and beard. In forensics, criminal identification having partial occlusion features is the most difficult task to perform. In this paper, a combination of the histogram of gradients (HOG) with Euclidean distance is proposed. Deep metric learning is the process of measuring the similarity between the samples using optimal distance metrics for learning tasks. In the proposed system, a deep metric learning technique like HOG is used to generate a 128d real feature vector. Euclidean distance is then applied between the feature vectors and a tolerance threshold is set to decide whether it is a match or mismatch. Experiments are carried out on disguised faces in the wild (DFW) dataset collected from IIIT Delhi which consists of 1000 subjects in which 600 subjects were used for testing and the remaining 400 subjects were used for training purposes. The proposed system provides a recognition accuracy of 89.8% and it outperforms compared with other existing methods.</span>

scholarly journals Photo-identification of horseshoe whip snakes (Hemorrhois hippocrepis, Linnaeus, 1758) by a semi-automatic procedure applied to wildlife management

2019 ◽  
pp. 304-307
Author(s):  
Andreu Rotger
Keyword(s):  
Wildlife Management ◽  
Traditional Method ◽  
Large Scale ◽  
Individual Recognition ◽  
Photo Identification ◽  
Animal Populations ◽  
Wildlife Control ◽  
Capture Recapture ◽  
Control Study ◽  
Natural Marks

Photo-identification is an increasingly used method for the study of animal populations. Natural marks such as coloration or scale pattern to identify individuals provide an inexpensive and less invasive alternative to conventional tagging methods. Photo-identification has previously been used to distinguish individual snakes, usually by comparing the pileus region. Nevertheless, this method is seldom used in capture-recapture studies. We show the effectiveness of photo-identification in snakes using specific software for individual recognition applied to a wildlife control study of horseshoe whip snakes. Photos were analysed with Automatic Photo Identification Suite (APHIS), which allowed us to compare the variability of head scale patterns surrounding the parietal shields instead of the traditional method of using large scale groups of the pileus. APHIS correctly identified 100 % of recaptures of snakes. Although further studies are needed, the variability of the surrounding scales of the pileus region seems a robust method to identify and differentiate individuals.

scholarly journals Revisiting animal photo‐identification using deep metric learning and network analysis

2021 ◽  
Author(s):  
Vincent Miele ◽  
Gaspard Dussert ◽  
Bruno Spataro ◽  
Simon Chamaillé‐Jammes ◽  
Dominique Allainé ◽  
...  
Keyword(s):  
Network Analysis ◽  
Metric Learning ◽  
Photo Identification ◽  
Deep Metric Learning

Dysarthric Speech Recognition Based on Deep Metric Learning

2020 ◽  
Author(s):  
Yuki Takashima ◽  
Ryoichi Takashima ◽  
Tetsuya Takiguchi ◽  
Yasuo Ariki
Keyword(s):  
Speech Recognition ◽  
Metric Learning ◽  
Deep Metric Learning ◽  
Dysarthric Speech

scholarly journals Ranked List Loss for Deep Metric Learning

2021 ◽  
pp. 1-1
Author(s):  
Xinshao Wang ◽  
Yang Hua ◽  
Elyor Kodirov ◽  
Neil M Robertson
Keyword(s):  
Metric Learning ◽  
Deep Metric Learning ◽  
Ranked List

scholarly journals Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 572
Author(s):  
Alan M. Luu ◽  
Jacob R. Leistico ◽  
Tim Miller ◽  
Somang Kim ◽  
Jun S. Song
Keyword(s):  
Neural Network ◽  
Amino Acid ◽  
Cytotoxic T Cells ◽  
Metric Learning ◽  
Binding Specificity ◽  
Class I ◽  
Multimodal Learning ◽  
Binding Prediction ◽  
Deep Metric Learning ◽  
Epitope Binding

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

A deep metric learning approach for weakly supervised loan default prediction1

2021 ◽  
pp. 1-13
Author(s):  
Kai Zhuang ◽  
Sen Wu ◽  
Xiaonan Gao
Keyword(s):  
Default Risk ◽  
Metric Learning ◽  
Real Life ◽  
Risk Scores ◽  
Loan Default ◽  
Default Prediction ◽  
Heterogeneous Features ◽  
Deep Metric Learning ◽  
Weakly Supervised ◽  
Decision Boundaries

To deal with the systematic risk of financial institutions and the rapid increasing of loan applications, it is becoming extremely important to automatically predict the default probability of a loan. However, this task is non-trivial due to the insufficient default samples, hard decision boundaries and numerous heterogeneous features. To the best of our knowledge, existing related researches fail in handling these three difficulties simultaneously. In this paper, we propose a weakly supervised loan default prediction model WEAKLOAN that systematically solves all these challenges based on deep metric learning. WEAKLOAN is composed of three key modules which are used for encoding loan features, learning evaluation metrics and calculating default risk scores. By doing so, WEAKLOAN can not only extract the features of a loan itself, but also model the hidden relationships in loan pairs. Extensive experiments on real-life datasets show that WEAKLOAN significantly outperforms all compared baselines even though the default loans for training are limited.

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