A knowledge-capturing approach for a cooperative intelligent image analysis system
Bibliographic record
Abstract
We present a generalized approach to dynamically incorporate high level knowledge into a cooperative intelligent image analysis framework. We developed this framework in our laboratory to provide a uniform interface to develop intelligent image analysis tools as well as to provide infrastructure facilities required by these tools in order to work cooperatively for accomplishing complex image analysis task goals. This framework is able to automatically generate processing plans which accomplish user defined image analysis task goals. The approach that we propose in this paper provides a flexible interface to develop the expertise of `image processing' tools. We provide two ways to develop this knowledge: 1) by taking feedback from an image processing expert about processing plans generated by the system; and 2) by accepting a processing plan which accomplishes a particular task from an expert user, and then extracting the high level knowledge encapsulated in this plan. The generalized nature of our approach allows each individual tool to use machine learning algorithms of its own interest in improving the knowledge-base. Preliminary results that we obtained from this work demonstrates the success of our approach.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".