A Novel 3D Object Categorization and Retrieval System Using Geometric Features
Bibliographic record
Abstract
In this paper, we propose a novel geometric features based method to categorize 3D models using probabilistic neural network and support vector machine classifiers. The employed features are extracted from face and vertex characteristics. In addition, we utilize the proposed features in 3D object retrieval. To achieve this end, each model is decomposed into a set of local/global geometrical features. We use histograms of two variables, i.e., deviation angle of normal vector on the object surface point from the vector that connect shape center to that point; and distance of object surface point from shape center. To achieve better separability of different models, mutual Euclidean distance histogram for the pairs of surface points is also used. The most advantage of using histogram to represent the features is that it shows the density of data and enables creating of low dimensional feature vector and consequently decreasing of computational cost in classification process. The effectiveness of our proposed 3D object categorization system has been evaluated on the generalized McGill 3D model dataset in terms of both accuracy and speed measures. Widespread experimental results and comparison with the other similar methods, demonstrate efficiency of the proposed approach to improve both accuracy and speed of categorization system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".