Impediments to general purpose Content Based Image search
Bibliographic record
Abstract
Challenges faced by prevailing text metadata paradigms for online image search have inspired overwhelming research in Content Based Image Retrieval (CBIR). A multitude of approaches have been introduced within the literature, yet relatively few image search engines have been made publicly available on the web. Aside from challenges facing the user, such as describing a visual query using keywords, or finding an appropriate example image to initiate a visual search, all systems must inevitably grapple with the sensory and semantic gaps [Smeulders et al. 2000], which essentially represent a loss of information in the abstraction process. In this work, we challenge commonly suggested approaches to improving CBIR and illustrate drawbacks of relying on textual data, as well as visual data, in general CBIR search. We provide cogent examples using online visual search engines Behold™, Tiltomo Beta, Pixilimar, and Riya™ Beta. These examples demonstrate the effect of semantic ambiguities in natural language, which extend to search terms and text tags.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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".