A New Hybrid Descriptor Based on Spatiogram and Region Covariance Descriptor
Bibliographic record
Abstract
Image descriptors play an important role in any computer vision system e.g.object recognition and tracking.Effective representation of an image is challenging due to significant appearance changes, viewpoint shifts, lighting variations and varied object poses.These challenges have led to the development of several features and their representations.Spatiogram and region covariance are two excellent image descriptors which are widely used in the field of computer vision.Spatiogram is a generalization of the histogram and contains some moments upon the coordinates of the pixels corresponding to each bin.Spatiogram captures richer appearance information as it computes not only information about the range of the function like histograms, also information about the (spatial) domain.However, there is a drawback that multi modal spatial patterns cannot be well modelled.Region covariance descriptor provides a compact and natural way of fusing different visual features inside a region of interest.However, it is based on a global distribution of pixel features inside a region and loses the local structure.In this paper, we aim toovercome the existing drawbacks of these descriptors.To this, we propose rspatiogram and then a new hybrid descriptor is presented which is combination of rspatiogram and traditional region covariance descriptors.The results show that our descriptors have the discriminative capability improved in comparison with other descriptors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".