Evaluation of the Power Consumption of Image Descriptors on Smartphone Platforms
Bibliographic record
Abstract
Important number of applications (APPs) used in smartphones use computer vision techniques that rely on limited battery power devices. Example of such techniques includes object detection algorithms such as HOG-SVM and Lbp-AdaBoost. In this paper, we evaluate the energy consumption of the most common descriptors and classifiers used in the literature. We begin our work by the implementation of the following algorithms: Lbp-AdaBoost, Haar-AdaBoost, HOG-AdaBoost and HOG-SVM on smartphones. Next, we run these algorithms to detect a specific object (for instance large animals). Several experiments are done to measure the energy consumption of each algorithm on two smartphones HTC ONE and Google Nexus 5. The energy consumption is evaluated using an application called PowerTutor. We find that faster detectors consume less energy, and vice versa. For instance, HOG-AdaBoost has the most energy-saving among the detectors used in this paper.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".