A Multi-Scale Hierarchical Codebook Method for Human Action Recognition in Videos Using a Single Example
Bibliographic record
Abstract
This paper presents a novel action matching method based on a hierarchical codebook of local spatio-temporal video volumes (STVs). Given a single example of an activity as a query video, the proposed method finds similar videos to the query in a video dataset. It is based on the bag of video words (BOV) representation and does not require prior knowledge about actions, background subtraction, motion estimation or tracking. It is also robust to spatial and temporal scale changes, as well as some deformations. The hierarchical algorithm yields a compact subset of salient code words of STVs for the query video, and then the likelihood of similarity between the query video and all STVs in the target video is measured using a probabilistic inference mechanism. This hierarchy is achieved by initially constructing a codebook of STVs, while considering the uncertainty in the codebook construction, which is always ignored in current versions of the BOV approach. At the second level of the hierarchy, a large contextual region containing many STVs (Ensemble of STVs) is considered in order to construct a probabilistic model of STVs and their spatio-temporal compositions. At the third level of the hierarchy a codebook is formed for the ensembles of STVs based on their contextual similarities. The latter are the proposed labels (code words) for the actions being exhibited in the video. Finally, at the highest level of the hierarchy, the salient labels for the actions are selected by analyzing the high level code words assigned to each image pixel as a function of time. The algorithm was applied to three available video datasets for action recognition with different complexities (KTH, Weizmann, and MSR II) and the results were superior to other approaches, especially in the cases of a single training example and cross-dataset action recognition.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".