Inverse scale invariant feature transform models for object recognition and image tagging.
Bibliographic record
Abstract
This thesis presents three novel image models based on Scale Invariant Feature Transform (SIFT) features and the k-Nearest Neighbors (k-NN) machine learning methodology. While SIFT features characterize an image with distinctive keypoints, the k-NN filters away and normalizes the keypoints with a two-fold goal: (i) compressing the image size, and (ii) reducing the bias that is induced by the variance of keypoint numbers among object classes. Object recognition is approached as a supervised machine learning problem, and the models have been formulated using Support Vector Machines (SVMs). These object recognition models have been tested for single and multiple object detection, and for asymmetrical rotational recognition. Finally, a hierarchical probabilistic framework with basic object classification methodology is formulated as a multi-class learning framework. This framework has been tested for automatic image annotation generation. Object recognition models were evaluated using recognition rate (rank 1) whereas the annotation task was evaluated using the well-known Information Retrieval measures: precision, recall, average precision and average recall.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".