Improving multi-view image classification using higher order information and triangulation embedding
Bibliographic record
Abstract
In this paper we take a look at extensions of the Bag of Words model developed within the last few years. Namely the aggregation of vector residuals known as VLAD encodings and Fisher kernels and assess their performance for the classification task using multiple views. We also take a look at the triangular embedding strategy for classification in the compression domain. Our work focuses on using the binary descriptor known as ORB. We are also able to show that triangular embedding is extremely fast and can provide the best performance without direct spatial encoding on the images themselves and we also demonstrate a novel approach to improve the triangulation accuracy that is less prone to overfitting than the traditional approach. We are able to show that higher order information provides improved performance in the multiple view setting. We finally take a look at the use of multiple view classification for fast image classification with a variable number of views and that our approach using a modified triangular embedding can overcome information loss and be used real-time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".