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Record W2049051812 · doi:10.3166/isi.10.1.81-99

A Video Content Independent Mining Algorithm for Evolved Rule-based Detection of Scene Boundaries

2005· article· en· W2049051812 on OpenAlex
Marios C. Angelides, Tek Sheng Kevin Lo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueIngénierie des systèmes d information · 2005
Typearticle
Languageen
FieldComputer Science
TopicVideo Analysis and Summarization
Canadian institutionsnot available
Fundersnot available
KeywordsContent (measure theory)Computer scienceArtificial intelligenceAlgorithmComputer visionPattern recognition (psychology)Data miningMathematics

Abstract

fetched live from OpenAlex

Segmentation of video data stream aims to divide the stream into temporally shorter, meaningful and manageable segments. This is the first step towards content-based multimedia database management, contented-based retrieval and browsing, and is very important to many other applications that aim to work with content. This paper presents a novel video mining algorithm that uses genetic programming for evolved rule-based scene boundary detection and whose key advantage is that is video content independent. Hence, the algorithm can be applied without modification to different video sequences just by feeding it different training data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.677

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.005
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.227
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it