A Human Visual System Based Temporal Model for Semantic Levels Categorization

Abstract

Humans can categorize an object in three ways. For example, a car can be categorized as vehicles (superordinate), ground vehicles (basic) or car (subordinate). Different semantic levels of categorization is referred to these three categorization modes. There are different speed and accuracy for a similar object in the classification of these levels. However, much research has been done in this context, the trend of these levels is still questionable as to the accuracy and reaction time and the reason for this difference. In this paper, we examine the order of these levels and the reason for their differences. Here we show the superordinate advantage is declared and after this level the base level and the subordinate level are expressed respectively. To this end, first, we design an experiment to examine the semantic levels in the human eye system. The result of this experiment is the superordinate advantage. Actually, at the superordinate level at the same time, the reaction time was lower than other levels, and the efficiency was higher than other levels. In addition, a computational model is introduced that has time and can classify semantic levels. The model is trained for ten categories in this study. These ten categories are considered as subordinate level and five levels of basic and two levels of superordinate are expressed. We found that, at higher levels, such as superordinate, more neurons participate in the clustering task, so outcome is faster and more accurate results. Moreover, the proposed model has been tested for inverted input images to verify the model.