Chordiogram image descriptor based on visual attention model for image retrieval
Bibliographic record
Abstract
A novel shape-based image retrieval is presented in this study. The foreground and background contents of images are strongly concealed, so they are represented individually to reduce their influence on each other in the proposed approach. The Otsu method is employed for segmenting the foreground from the background, and the saliency map and edge map are then clearly identified. Saliency reduces the time cost for feature computation, so salient edges are computed for the foreground and background images based on the selective visual attention model. Autocorrelation-based chordiogram image descriptors are computed separately for the foreground and background images, which are then combined in a hierarchical manner to form the proposed new descriptor. This approach avoids the concealment of foreground and background information, and the new descriptor is rich in geometric and its underlying texture, structure and spatial information. The proposed novel shape-based descriptor performs considerably better than conventional descriptors at content-based image retrieval. The proposed shape descriptor were extensively tested at image retrieval based on the Gardens Point Walking, St Lucia, University of Alberta Campus, Corel 10 k, and self-photographed image data sets. The precision and recall values were compared for the proposed and state-of-the-art-approaches when applied for shape-based image retrieval from these databases. The proposed shape descriptor provided satisfactory retrieval results in the experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".