A flexible framework for developing a cooperative intelligent image analysis system
Bibliographic record
Abstract
We present a software framework for developing a flexible image analysis system. This framework provides a uniform interface to develop intelligent image analysis tools as well as infrastructure facilities required by these tools for working cooperatively with other tools. This system first automatically generates a processing plan to accomplish a user defined task, and then executes that plan to produce results. Each processing tool encapsulates an image processing algorithm as well as knowledge about this algorithm. Tools use this knowledge to evaluate their suitability to handle a given task. This approach allows each processing tool the ability of selectively accepting appropriate tasks that belong to its domain. A processing tool is also able to define subtasks, which must be accomplished to refine input data as required by its underlying image processing algorithm. These subtasks can be broadcast among other tools and enlist appropriate individuals to accomplish task goals. Our framework is able to accept image analysis tasks defined using abstract conceptual terms used in application domains, and uses production rules to expand detailed fprms of these terms. This facility allows successfully defining an image analysis task without specifying all low level details. Preliminary results that we obtained from this framework demonstrated 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".