A Comparative Study of Sparseness Measures for Segmenting Textures
Bibliographic record
Abstract
The concept of sparseness has played an important role in classical signal processing applications such as the acquisition, sampling, and compression of high-dimensionality signals, as well as in various machine learning techniques. Computer vision applications have also benefited in more recent years from sparse representations, which can help recover semantic information from images. In this paper, we shed a unique light on the concept of sparseness and propose a comparative study of four popular sparseness measures applied in a novel way to the problem of texture segmentation. Low-dimensional, contextual, multi-resolution descriptors are derived directly from the sparseness of the pixels' responses to a Gabor filter bank, unlike traditional sparseness-based approaches, we do not impose constraints on or make assumptions about the sparseness of the data or of their representation. Textured images are segmented through pixel labelling via supervised machine learning using the sparseness-based descriptors. The behaviour of the four compared sparseness measures, namely Hoyer's measure, the Gini index, the kurtosis, and the normalized hyperbolic tangent, is analyzed with respect to general rules and desirable attributes using synthetic examples, and is evaluated for texture segmentation problems on the public Outex dataset with respect to texture classes and pixel pattern categories. We found that although the four measures all intend to capture the same information, they yield very different segmentation results, and we recommend the Gini index as the sparseness measure of choice for texture segmentation problems.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".