Computational Intellegence Techniques and their Applications in Content-Based Image Retrieval
Why this work is in the frame
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Bibliographic record
Abstract
The main focus of this paper is to present a methodology for optimizing relevance identification in content-based image retrieval (CBIR) systems through the principle of feature weight detection. The purpose of relevance identification is to find a collection of images that are statistically similar to, or match with, an original query image within a large visual database. The novelty of this scheme is two-fold: using a base-10 genetic algorithm method to accurately determine the contribution of individual feature vectors for a successful retrieval in the so-called feature weight detection process, and defining a new unsupervised learning algorithm, the directed self-organizing tree map (DSOTM), for the purpose of classification in the automatic relevance identification module of the search engine. Comprehensive experiments demonstrate feasibility of the proposed methodology
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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 | 0.001 |
| 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 it