Content-Based Image Retrieval Using Adaptive CIE Color Feature Fusion
Bibliographic record
Abstract
This work proposes a novel content-based image retrieval framework using adaptive weight feature fusion in the International Commission on Illumination (CIE) color space.To enhance the weights of the saliency region features of an image, an adaptive wrapper model is proposed for the adaptive feature selection.Initially, the images are transferred to the CIE color space, i.e., the L*, a*, b* color space.The local binary model (LBP) texture features of all four channels are analyzed class-wise.For each class, the weights of the LBP features for a* and b* axis are calculated dynamically as per their class variance.The weighted LBP features along a* and b* axis are merged, which is referred to as the LBPCW feature in the CIE color space.To test the performance of the proposed LBPCW feature we developed a CBIR system, here two standard classifiers i.e.Support Vector Machine (SVM), and Naï ve Bayes (NB) is used for classification and Euclidian distance measure is used for image retrieval.The model is tested with two public datasets Wang-1K and Corel-5K.It is observed that our proposed LBPCW feature outperforms LBP and local binary pattern with saliency map (LBPSM) features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".