Goal-oriented optimal subset selection of correlated multimedia streams
Bibliographic record
Abstract
A multimedia analysis system utilizes a set of correlated media streams, each of which, we assume, has a confidence level and a cost associated with it, and each of which partially helps in achieving the system goal. However, the fact that at any instant, not all of the media streams contribute towards a system goal brings up the issue of finding the best subset from the available set of media streams. For example, a subset of two video cameras and two microphones could be better than any other subset of sensors at some time instance to achieve a surveillance goal (e.g. event detection). This article presents a novel framework that finds the optimal subset of media streams so as to achieve the system goal under specified constraints. The proposed framework uses a dynamic programming approach to find the optimal subset of media streams based on three different criteria: first, by maximizing the probability of achieving the goal under the specified cost and confidence; second, by maximizing the confidence in the achieved goal under the specified cost and probability with which the goal is achieved; and third, by minimizing the cost to achieve the goal with a specified probability and confidence. Each of these problems is proven to be NP-Complete. From an AI point of view, the solution we propose is heuristic-based, and for each criterion, utilizes a heuristic function which for a given problem, combines optimal solutions of small-sized subproblems to yield a potential near-optimal solution to the original problem. The proposed framework allows for a tradeoff among the aforementioned three criteria, and offers the flexibility to compare whether any one set of media streams of low cost would be better than any other set of higher cost, or whether any one set of media streams of high confidence would be better than any other set of low confidence. To show the utility of our framework, we provide the experimental results for event detection in a surveillance scenario.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".