Visual Similarity Search of Cattle Brands Using Deep Learning on Binary Representations
Bibliographic record
Abstract
Identifying cattle brands is a challenging visual task due to variability in branding styles, degradation over time, and the absence of standardized, large-scale datasets. To address this, we propose a deep learning-based Content-Based Image Retrieval (CBIR) framework specifically designed for this problem. Our system matches hand-drawn or digitized query sketches of cattle brands against a reference database of binarized brand symbols using learned visual embeddings for similarity search. To support training and rigorous evaluation, we assembled the Apporteira Cattle Brand Dataset, comprising 5,233 clean binary brand templates, 1,454 hand-drawn sketches, and over 627,000 augmented images simulating real-world distortions via rotations, morphological operations, and homographic transformations. This dataset has been made publicly available to enable reproducible research and benchmarking. We evaluate classical feature descriptors, pretrained convolutional neural networks (ResNet-50, MobileNet, VGG), and a fine-tuned VGG-16 model adapted to this domain. Experiments are reported with standard CBIR metrics, including mean Average Precision (mAP) and Top-$k$accuracy, together with retrieval efficiency using Facebook AI Similarity Search (FAISS) to assess scalability. Our fine-tuned model achieves 79.71% Top-1 and 97.18% Top-10 accuracy, substantially outperforming generic CNN baselines and handcrafted methods. The results highlight the effectiveness of task-specific fine-tuning, showing consistent gains even when baselines perform strongly at higher ranks, and demonstrate the system's robustness to symbol variation, offering a scalable solution for livestock identification, rural security enforcement, and brand registry automation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".