Statistical similarity measures in image retrieval systems with categorization & amp; block based partition
Bibliographic record
Abstract
This paper presents a novel approach of similarity matching in image retrieval based on the distribution of joint feature vectors of color and texture features. Mean vectors and covariance matrices are computed from feature distributions of training samples with known categories and from individual images with varying partitions on the assumption that, distributions are multivariate Gaussian. Statistical distance measures utilize these parameters in similarity matching functions to minimize the probability of retrieval error. For category specific retrieval, a multi-class support vector machine (SVM) is trained on the samples to predict the categories of query and database images. Based on the online prediction, precompiled category specific statistical parameters are utilized in similarity measure functions. For partition specific retrieval, individual images are partitioned into non-overlapping blocks of different sizes and a joint feature vector of color and texture features are extracted from each block to generate the distribution and estimate the parameters. Experimental results on a generic image database with ground truth are reported. Performances of two statistical distance measures, namely Bhattacharyya and Mahalanobis are evaluated and compared with Euclidian distance measure, which show the effectiveness of the proposed technique.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".