Image object search combining color with Gabor wavelet shape descriptions
Bibliographic record
Abstract
An image and object search and retrieval algorithm is devised that combines color and spatial information. Spatial characteristics are described in terms of Wiskott’s jets formulation, based on a set of Gabor wavelet functions at varying scales, orientations and locations. Color information is first converted to a form more impervious to illumination color change, reduced to 2D, and encoded in a histogram based on a new stretched chromaticity space for which all bins are populated. An image database of 27,380 images is devised by replicating 2,738 JPEG images by a set of transforms that include resizing, various cropping attacks, JPEG quality changes, aspect ratio alteration, and reducing color to grayscale. Correlation of the complete encode vector is used as the similarity measure. For both searches with the original image as probe within the complete dataset, and with the altered images as probes with the original dataset, the grayscale, the stretched, and the resized images had near-perfect results. The most formidable challenge was found to be images that were cropped both horizontally as well as vertically. The algorithm’s ability to identify <i>objects</i>, as opposed to just images, is tested. In searching for images in a set of 4 classifications, the jets were found to contribute most analytic power when objects with distinctive spatial characteristics were the target.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".