Bibliographic record
Abstract
Image and video content analysis is an interesting, meaningful and challenging topic. In recent years much of the research effort in the multimedia field focuses on indexing and retrieval. Semantic gap between low-level features and high-level content is a bottleneck in most systems. To bridge the semantic gap, new content analysis models need to be developed. In this thesis, algorithms based on a relatively new graphical model, called the conditional random field (CRF) model, are developed for two closely-related problems in content analysis: image labeling and video content analysis. The CRF model can represent spatial interactions in image labeling and temporal interactions in video content analysis. New feature functions are designed to better represent the feature distributions. The mixture feature functions are used in image labeling for databases with nature images, and the independent component analysis (ICA) mixture function is applied in sports video content analysis. The spatial dependence of image parts and the temporal dependence of video frames can be explored by the CRF model more effectively using new feature functions. For image labeling with large databases, the content-based image retrieval method is combined with the CRF image labeling model successfully.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".