Can Contextual Information Improve Scene Classification Performance?
Bibliographic record
Abstract
Bag of visual words (BoVW) remains a very competitive representation in the domain of scene classification. In this framework, extracting SIFT descriptors on a dense grid of pixels has shown to lead to a better performance. However, due to the nature of SIFT as an edge-based descriptor, computing SIFT on homogeneous regions might result in non-stable region descriptors. The suggested solution in the literature is ignoring and discarding these regions descriptors from the final image level representation. We argue that homogeneous regions contain valuable scene information if represented appropriately. In such manner, a simple yet effective method to model homogeneous image regions is proposed. We call these models contextual information, where their importance on scene classification is investigated. The final image-level representation is the stacking of feature vectors from homogeneous and non-homogeneous regions. The proposed approach is validated on two de-facto standard databases for scene classification: Fifteen Scene Categories and 67 Indoor Scenes datasets. Experimental results on these two datasets show the effectiveness of the proposed model.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| 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".