Image Description Using the Relation Between Color and Texture in Retrieval Task
Bibliographic record
Abstract
In the past years, significant efforts have been made for new theories and models of descriptors for Content-Based Image Retrieval systems and many effective descriptors, which use color and texture, have been established. This article presents the analysis and modifications of descriptors that use color and texture for the image retrieval task. To provide a complete detailed, and fair analysis, exposing weaknesses in descriptors and ideas to correct them. We evaluated descriptors that use color and texture, with image sets and metrics found in the literature. We compared classical descriptors that only use one low-level characteristic with descriptors that use color and texture. The analysis showed discrepancies between the model and the implementation of one of the descriptors, as well as the descriptors with the best performance, their main weaknesses, and complications when we trying to correct them. likewise, we present variants that improve the image retrieval in some cases.
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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 | 0.000 |
| 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 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".