Bibliographic record
Abstract
The overall objective of this paper is to present an extended application of Content-Based Image Retrieval (CBIR) over distributed (decentralized) image databases. Traditional image retrieval system design has implicitly relied on a local (centralized) query server, such as IBM's QBIC [1], Columbia's VisualSEEk [2], MIT's PhotoBook [3], and UCSD's Viagem™ [4]. With the growing popularity of the internet, however, the focus of the research in this area has been shifted toward content query over distributed databases. Ng et al. [5] has studied a peer-clustering model for the query with the assumption that the image collection at each peer node falls under one category. Even though, this assumption is effective for preliminary studies, it is unable to implant the practical end-user behaviors. Lee et al. [6] has introduced a novel approach to study practical scenarios where multiple image categories exist in each individual database in the distributed storage network. This approach is proven to be an effective method to improve retrieval precision via identifying the community neighborhood who shares similar content collection. The main focus of this paper is to study behavior of a CBIR engine in an interactive distributed environment. In the proposed approach, the query image is sent to all registered databases in the network. Response of each database is then collected and transferred to a local server where a supervised relevance identification approach is applied to identify final outcome of the search. Response of each database is quantified via estimating the statistical resemblance of top image candidates to the existing query image. Comprehensive experiments demonstrate feasibility of the proposed methodology.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".