Directional detail histogram for content based image retrieval
Bibliographic record
Abstract
With the widespread availability of personal computers and the staggering amount of digital imagery available, recent investigation has focused on the storage, query and retrieval of images from large image databases. Methods, which are employed presently, precompute image indices, which are deemed to be statistical representations of image information. Queries of colour, shape, texture, etc., are then performed directly on these indices to find valid images. We propose a new indexing technique which calculates the histogram of the directional detail content in a given image. We apply wavelet theory and multiresolution analysis to extract the directional information from an image. This information is mapped into 3-dimensional vectors and histograms are calculated, allowing image query using colour histogram techniques to be directly applied. Our technique is capable of querying and searching a database for images based on attributes such as smoothness, randomness and horizontal, vertical and diagonal edges at varying resolutions.
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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 | 0.000 | 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 | 0.000 |
| 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 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".