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<p>The quality of a semantic annotation is typically measured with its averaged class-accuracy value, whose computation requires scarce ground-truth annotations. We observe that humans accumulate knowledge through their vision and believe that the quality of a semantic annotation is proportionally related to its compatibility with the vision-based knowledge. We propose a knowledge-compatibility benchmarker, whose backbone is a regression machine. It takes as input a semantic annotation and the vision-based knowledge, then outputs an estimate of the corresponding averaged class-accuracy value. The knowledge encodes three kinds of information, namely: cooccurrence statistics, scene properties and relative positions. We introduce three types of feature vectors for regression. Each specifies the characteristics of a probability vector that captures the compatibility between an annotation and each kind of the knowledge. Experiment results show that the Gradient Boosting regression outperforms the ν -Support Vector regression. It achieves best performance at an R<sup>2</sup>-score of 0.737 and an MSE of 0.034. This indicates not only that the vision-based knowledge resembles humans’ common sense but also that the feature vector for regression is justifiable.</p></abstract>

The quality of a semantic annotation is typically measured with its averaged class-accuracy value, whose computation requires scarce ground-truth annotations. We observe that humans accumulate knowledge through their vision and believe that the quality of a semantic annotation is proportionally related to its compatibility with the vision-based knowledge. We propose a knowledge-compatibility benchmarker, whose backbone is a regression machine. It takes as input a semantic annotation and the vision-based knowledge, then outputs an estimate of the corresponding averaged class-accuracy value. The knowledge encodes three kinds of information, namely: cooccurrence statistics, scene properties and relative positions. We introduce three types of feature vectors for regression. Each specifies the characteristics of a probability vector that captures the compatibility between an annotation and each kind of the knowledge. Experiment results show that the Gradient Boosting regression outperforms the ν -Support Vector regression. It achieves best performance at an R2-score of 0.737 and an MSE of 0.034. This indicates not only that the vision-based knowledge resembles humans’ common sense but also that the feature vector for regression is justifiable.

A Novel Knowledge-Compatibility Benchmarker for Semantic Segmentation

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"A Novel Knowledge-Compatibility Benchmarker for Semantic Segmentation"}

The quality of a semantic annotation is typically measured with its averaged class-accuracy value, whose computation requires scarce ground-truth annotations....

A Novel Knowledge-Compatibility Benchmarker for Semantic Segmentation

Publicado en línea: 01 jun 2015

Páginas: 1284 - 1312

Recibido: 15 ene 2015

Aceptado: 24 mar 2015

DOI: https://doi.org/10.21307/ijssis-2017-807

© 2015 Vektor Dewantob et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.