Improving Recall of Browsing Sets in Image Retrieval from a Semiotics Perspective
Bibliographic record
Abstract
The purpose of dissertation is to utilize connotative messages for enhancing image retrieval and browsing. By adopting semiotics as a theoretical tool, this study explores problems of image retrieval and proposes an image retrieval model. The semiotics approach conceptually demonstrates that: 1) a fundamental reason for the dissonance between retrieved images and user needs is representation of connotative messages, and 2) the image retrieval model which makes use of denotative index terms is able to facilitate users to browse connotatively related images effectively even when the users' needs are potentially expressed in the form of denotative query. Two experiments are performed for verifying the semiotic-based image retrieval model and evaluating the effectiveness of the model. As data sources, 5,199 records are collected from Artefacts Canada: Humanities by Canadian Heritage Information Network, and the candidate terms of connotation and denotation are extracted from Art & Architecture Thesaurus. The first experiment, by applying term association measures, verifies that the connotative messages of an image can be derived from denotative messages of the image. The second experiment reveals that the association thesaurus which is constructed based on the associations between connotation and denotation facilitates assigning connotative terms to image documents. In addition, the result of relevant judgments presents that the association thesaurus improves the relative recall of retrieved image documents as well as the relative recall of browsing sets. This study concludes that the association thesaurus indicating associations between connotation and denotation is able to improve the accessibility of the connotative messages. The results of the study are hoped to contribute to the conceptual knowledge of image retrieval by providing understandings of connotative messages within an image and to the practical design of image retrieval system by proposing an association thesaurus which can supplement the limitations of the current content-based image retrieval systems (CBIR).
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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 | 0.001 |
| 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".