A robust object detection approach using boosted anisotropic multiresolution analysis
Bibliographic record
Abstract
In an unconstrained environment, adaptive classifiers produce improved recognition. Fast discrete curvelet transform has recently gained attention due to its ability to capture singularities along curves far away from smooth regions. Therefore, curvelet coefficients contain enhanced representation of image details at different scales and orientations. We propose a new approach for object class detection based on curvelet feature subspace obtained using Kernel PCA (KPCA) and learned using AdaBoost scheme [1]. The first contribution of current paper is a unique representation of an image called curvelet feature subspace that preserves global structure, and supports reliable detection of singularities along curves which play a considerably important role in recognition. Second contribution of our proposed method is an adaptive selection of features obtained using anisotropic style multiresolution analysis for robust object detection of varied inter-class, and intra-class attributes. Our proposed method achieved better detection rate compared to state-of-the-art schemes.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".