Information Mining from Multimedia Databases
Bibliographic record
Abstract
Welcome to the special issue on "Information mining from multimedia databases."The main focus of this issue is on information mining techniques for the extraction and interpretation of semantic contents in multimedia databases.The advances in multimedia production technologies have resulted in a rapid proliferation of various forms of media data types on the Internet.Given these high volumes of multimedia data, it is thus essential to extract and interpret their underlying semantic contents from the original signal-based representations without the need for extensive user interaction, and the technique of multimedia information mining plays an important role in this automatic content interpretation process.Due to the spatio-temporal nature of most multimedia data streams, an important requirement for this information mining process is the accurate extraction and characterization of salient events from the original signal-based representation, and the discovery of possible relationships between these events in the form of high-level association rules.The availability of these high-level representations will play an important role in applications such as content-based multimedia information retrieval, preservation of cultural heritage, surveillance, and automatic image/video annotation.For these problems, the main challenges are in the design and analysis of mapping techniques between the signal-level and semantic-level representations, and the adaptive characterization of the notion of saliency for multimedia events in view of its dependence on the preferences of individual users and specific contexts.The focus of the first two papers is on the automatic analysis and interpretation of video contents.X.-P.Zhang and Chen describe a new approach to extracting objects from video sequences which is based on spatio-temporal independent component analysis and multiscale analysis.Specifically, spatio-temporal independent component analysis is
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".