Perceptual Shape-Based Natural Image Representation and Retrieval
Why this work is in the frame
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Bibliographic record
Abstract
Human visual recognition is based largely on shape, yet effectively using shapes in natural image retrieval is a challenging task. Most existing methods are based on the geometric equations of curves computed from processing an entire image. These processes are computationally intensive, lack flexibility and do not take advantage or with minimum use of the Gestalt rules of human vision. By applying certain mechanisms based on the human visual perception process, our methods extract generic shape features from real world images. We extract and group perceptually significant segments and use their properties to create a Euclidean distance matrix for image retrieval. As all the computing is based on simple calculation and one pixel width edges instead of the whole image, this method provides a novel and efficient image feature representation. Testing on standard benchmark datasets and comparison with other well-known methods show this shape analysis method using only compact feature vectors performs well and robustly for real world images.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it