Bibliographic record
Abstract
With nearly twenty years of intensive study on the content-based image retrieval and annotation, the topic still remains difficult. By and large, the essential challenge lies in the limitation of using low-level visual features to characterize the semantic information of images, commonly known as the semantic gap. To bridge this gap, various approaches have been proposed based on the incorporation of human knowledge and textual information as well as the learning techniques utilizing the information of different modalities. At the same time, contextual information which represents the relationship between different real world/conceptual entities has shown its significance with respect to recognition tasks not only through real life experience but also scientific studies. In this chapter, the authors first review the state of the art of the existing works on image annotation and retrieval. Moreover, a general Bayesian framework which integrates content and contextual information and its application to both image annotation and retrieval are elaborated. The contextual information is considered as the statistical relationship between different images and different semantic concepts for image retrieval and annotation, respectively. The framework has efficient learning and classification procedures and the effectiveness is evaluated based on experimental studies, which demonstrate its advantage over both content-based and context-based approaches.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".