Image similarity measurement from sparse reconstruction errors
Bibliographic record
Abstract
This paper presents a new approach to measuring the similarity between two images using sparse reconstruction. Our approach alleviates the difficulty of selecting and extracting suitable features from images which usually requires domain-specific knowledge. The proposed measure, the Sparse SNR (SSNR), does not use any prior knowledge about the data type or the application. SSNR is generic in the sense that it is applicable, without modification, to a variety of problems involving different types of images. Given a pair of images, a set of basis vectors (dictionary) is learnt for each image such that each image can be represented as a linear combination of a small number of its dictionary elements. Each image is reconstructed by two dictionaries - the one trained on the image itself and the second - trained on the other image. We develop a novel similarity measure based on the resulting reconstruction errors. To the best of our knowledge, this is the first attempt to develop a sparse reconstruction-based similarity measure. Excellent classification, clustering and retrieval results are achieved on benchmark datasets involving facial images and textures.
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.
How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".