Improving codebook generation for action recognition using a mixture of Asymmetric Gaussians
Bibliographic record
Abstract
Human activity recognition is a crucial area of computer vision research and applications. The goal of human activity recognition aims to automatically analyze and interpret ongoing events and their context from video data. Recently, the bag of visual words (BoVW) approach has been widely applied for human action recognition. Generally, a representative corpus of videos is used to build the Visual Words dictionary or codebook using a simple k-means clustering approach. This visual dictionary is then used to quantize the extracted features by simply assigning the label of the closest cluster centroid using Euclidean distance between the cluster centers and the input descriptor. Thus, each video can be represented as a frequency histogram over visual words. However, the BoVW approach has several limitations such as its need for a predefined codebook size, dependence on the chosen set of visual words, and the use of hard assignment clustering for histogram creation. In this paper, we are trying to overcome these issues by using a mixture of Asymmetric Gaussians to build the codebook. Our method is able to identify the best size for our dictionary in an unsupervised manner, to represent the set of input feature vectors by an estimate of their density distribution, and to allow soft assignments. Furthermore, we validate the efficiency of the proposed algorithm for human action recognition.
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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 | 0.001 |
| 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".