Efficient color image retrieval method using deep stacked sparse autoencoder
Bibliographic record
Abstract
The recent advancement in deep learning-based approaches vastly outperforms the traditional image descriptors. Deep learning models, such as residual networks (ResNet), are well known for finding salient features. Although effective, high-level description often has a high dimensionality that increases computational overhead. The autoencoders find the useful approximation of the input data without losing critical information. Considering this, we propose a content-based image retrieval system for natural color images using a deep stacked sparse autoencoder (DSSA). The DSSA model learns latent features in an unsupervised way from the high-level description obtained using ResNet. The DSSA model achieves a nearly 50% reduction in size compared with the full-length features for the simple distance-based retrieval approach while increasing accuracy. The image retrieval efficacy of the learned latent features is also evaluated for two classifier-based methods using a Softmax classifier. Further, this study investigates the impact of unsupervised feature learning on retrieval using three benchmark natural color image databases of varying complexities, viz., Corel-1K, Corel-10K, and Canadian Institute for Advanced Research (CIFAR)-10. The latent features learned by the DSSA model with the fuzzy class membership-based retrieval method achieve promising improvements and yield a highly competitive retrieval performance with the large-size CIFAR-10 database.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".