The Sloop System for Individual Animal Identification with Deep Learning

2020/03/01 by Kshitij Bakliwal, Bakliwal, Kshitij, Sai Ravela +1
Biochemistry, Genetics and Molecular Biology · Environmental Science · #68T45 #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #I.4.10 #I.4.9 #Identification and Quantification in Food #Information Retrieval (cs.IR) #Marine animal studies overview #Wildlife Ecology and Conservation

paper · pdf · doi:10.48550/arxiv.2003.00559

openalex publication_date 2020/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The MIT Sloop system indexes and retrieves photographs from databases of non-stationary animal population distributions. To do this, it adaptively represents and matches generic visual feature representations using sparse relevance feedback from experts and crowds. Here, we describe the Sloop system and its application, then compare its approach to a standard deep learning formulation. We then show that priming with amplitude and deformation features requires very shallow networks to produce superior recognition results. Results suggest that relevance feedback, which enables Sloop's high-recall performance may also be essential for deep learning approaches to individual identification to deliver comparable results.

Citations

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