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Record W2112801673 · doi:10.1109/icassp.2004.1327291

Practical MPEG-7 image indexing & retrieval for undergraduates

2004· article· en· W2112801673 on OpenAlexaff
P. Andoutsos, Azadeh Kushki, A.N. Venetsanopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Analysis and Summarization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSearch engine indexingComputer scienceMultimediaASCIIInformation retrievalSet (abstract data type)ParsingIndex (typography)Image retrievalImage (mathematics)Artificial intelligenceWorld Wide WebProgramming language

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.434
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.328
Teacher spread0.284 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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