Organizing and browsing photos using different feature vectors and their evaluations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Two-dimensional similarity-based image organizing studies how to place photos within 2D virtual canvas based on their visual contents so that the users can easily locate the desired photos. As an extension to our previous work [10], several improvements are made in this paper to allow better photo browsing experiences. For example, the new approach pre-orders all the photos so that a consistent set of photos is selected for display. This solves the photo flickering problem of our previous approach, which uses K-mean algorithm to dynamically select photos.The main focus of this paper however is on the evaluation of the effectiveness of different feature vectors for 2D photo organization. A performance metric is proposed to measure how well photos with similar visual contents are grouped together on the 2D canvas. Feature vectors generated using eight different low-level feature extraction approaches are tested. The evaluation results reveal the pros and cons of different feature extraction approaches, which can be a useful guide for developing new feature vectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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