Visual Keyword-based Image Retrieval using Latent Semantic Indexing, Correlation-enhanced Similarity Matching and Query Expansion in Inverted Index
Bibliographic record
Abstract
This paper presents an image retrieval framework with scalable image representation and inverted file-based indexing by incorporating automatically generated visual keywords. A codebook of visual keywords is implemented adopting a self-organizing map (SOM)-based vector quantization on the feature space of segmented image regions. The codebook is utilized to represent images by calculating the keyword statistics in the individual images as well as in the collection as a whole. To reduce the dimensionality of the sparse feature vector, latent semantic indexing technique is applied and a similarity matching function is proposed by exploiting the correlation between visual keywords. A query expansion strategy is also proposed in the inverted index based on the topology preserving structure of the SOM. Experimental results over a collection of 5000 general photographic images demonstrate the efficiency and effectiveness of the proposed approach compared to the low-level histogram-based approaches
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".