An efficient framework on large-scale video genre classification
Bibliographic record
Abstract
Efficient data mining and indexing is important for multimedia analysis and retrieval. In the field of large-scale video analysis, effective genre categorization plays an important role and serves one of the fundamental steps for data mining. Existing works utilize domain-knowledge dependent feature extraction, which is limited from genre diversification as well as data volume scalability. In this paper, we propose a systematic framework for automatically classifying video genres using domain-knowledge independent descriptors in feature extraction, and a bag-of-visualwords (BoW) based model in compact video representation. Scale invariant feature transform (SIFT) local descriptor accelerated by GPU hardware is adopted for feature extraction. BoW model with an innovative codebook generation using bottom-up two-layer K-means clustering is proposed to abstract the video characteristics. Besides the histogram-based distribution in summarizing video data, a modified latent Dirichlet allocation (mLDA) based distribution is also introduced. At the classification stage, a k-nearest neighbor (k-NN) classifier is employed. Compared with state of art large-scale genre categorization in, the experimental results on a 23-sports dataset demonstrate that our proposed framework achieves a comparable classification accuracy with 27% and 64% expansion in data volume and diversity, respectively.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".