Practical MPEG-7 image indexing & retrieval for undergraduates
Bibliographic record
Abstract
A practical component to an upper year electrical and computer engineering course in information engineering or multimedia systems is presented. Developed to introduce students to image indexing and retrieval, the experiment uses a Microsoft Windows binary of the freely available MPEG-7 Experimentation Model from ISO for image description. The experiment is broken into three interdependent stages spanning a three week period. Each stage demonstrates various concepts associated with image indexing and retrieval. Students require C/C++ programming skills to parse binary encoded MPEG-7 descriptions, output ASCII description data, and perform retrievals on indexed information. The laboratory is distributed in nature, and each participating student is made to work on a small, mutually exclusive image set. The experiment drives home the urgency of the media indexing problem in light of the rapidly growing market need for multimedia management applications resulting from the huge influx of multimedia data. Through feedback, student views indicate that despite being long and time consuming, the experiment provides an interesting, original and stimulating experience.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.031 |
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".