Information Fusion for Human Action Recognition via Biset/Multiset Globality Locality Preserving Canonical Correlation Analysis
Bibliographic record
Abstract
In this paper, we study the problem of human action recognition, in which each action is captured by multiple sensors and represented by multisets. We propose two novel information fusion techniques for fusing the information from multisets. The first technique is biset globality locality preserving canonical correlation analysis (BGLPCCA), which aims to learn the common feature subspace between two sets. The second technique is multiset globality locality preserving canonical correlation analysis (MGLPCCA), which aims to deal with three or more sets. The proposed BGLPCCA and MGLPCCA are able to learn a low-dimensional common subspace that preserves the local and global structures of data samples. Moreover, two novel descriptors are presented for both depth and skeleton. We then propose a new human action recognition framework employing the proposed BGLPCCA or MGLPCCA to learn the shared subspace from multiple sets of features including skeleton, depth, and optical flow. Extensive experiments on five publicly available datasets (MSR Action3D, UTD multimodal human action dataset, multimodal action database, Kinect activity recognition dataset, and SBU Kinect interaction dataset) demonstrate the effectiveness of the proposed framework.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".