Places Clustering of Full-Length Film Key-Frames Using Latent Aspect Modeling Over SIFT Matches
Bibliographic record
Abstract
An improved unsupervised classification method to extract and link places features and cluster recurrent physical locations (key-places) within a movie is presented. Our approach finds links between key frames of a common key-place based on the use of a probabilistic latent space model over the possible local matches between the key frames image set. This allows the extraction of significant groups of local matching descriptors that may represent characteristic elements of a key-place. An exhaustive evaluation of our approach was conducted on in-house and public image datasets, as well as on full-length movies. Results revealed that our method is very efficient for near-duplicate object/background detection with weak overlap. Performance measurements on full-length movies indicate a recognition rate of about 75% on the key-places clustering with a false alarm rate (FAR) of approximately 2%.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".