Image indexing and retrieval: Current projects and a comprehensive research agenda for the future
Bibliographic record
Abstract
Abstract This panel focuses on the major research questions needing further exploration in the areas of image organization, retrieval, and use. The panel will first have short presentations on several ongoing image research projects and presenters will briefly comment on their current research, new tools and approaches to image indexing, and the broader research areas they address. The panel will then move into an interactive mode and the moderator will present a brief outline of a broad‐based image research agenda for panel/audience dialogue, through which the agenda will be expanded and refined. In particular, current research in image indexing and retrieval focuses on the conference topic of “Thriving on Diversity – Information Opportunities in a Pluralistic World” as many newer tools (e.g., geotagging) and many new voices are joining in the image description process. The brief presentations relate to topics now appearing in the image indexing literature: information and knowledge behavior in diverse contexts, social networking in a linguistically and culturally rich environment, and challenges of harmony versus hegemony, as well as quality and relevance to particular audiences.
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 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 | 0.061 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.028 | 0.058 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".