A novel knowledge-compatibility benchmarker for semantic segmentation

Vektor Dewanto, Aprinaldi, Zulfikar Ian, Wisnu Jatmiko

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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 n-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.

Original languageEnglish
Pages (from-to)1284-1312
Number of pages29
JournalInternational Journal on Smart Sensing and Intelligent Systems
Issue number2
Publication statusPublished - 2015


  • Averaged class accuracy
  • Knowledge-compatibility benchmarker
  • Regression
  • Semantic segmentation
  • Vision-based knowledge


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