文章基本信息

标题：A Computational Approach towards Visual Object Recognition at Taxonomic Levels of Concepts
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作者：Zahra Sadeghi ; Babak Nadjar Araabi ; Majid Nili Ahmadabadi 等
期刊名称：Computational Intelligence and Neuroscience
印刷版ISSN：1687-5265
电子版ISSN：1687-5273
出版年度：2015
卷号：2015
DOI：10.1155/2015/905421
出版社：Hindawi Publishing Corporation
摘要：It has been argued that concepts can be perceived at three main levels of abstraction. Generally, in a recognition system, object categories can be viewed at three levels of taxonomic hierarchy which are known as superordinate, basic, and subordinate levels. For instance, “horse” is a member of subordinate level which belongs to basic level of “animal” and superordinate level of “natural objects.” Our purpose in this study is to take an investigation into visual features at each taxonomic level. We first present a recognition tree which is more general in terms of inclusiveness with respect to visual representation of objects. Then we focus on visual feature definition, that is, how objects from the same conceptual category can be visually represented at each taxonomic level. For the first level we define global features based on frequency patterns to illustrate visual distinctions among artificial and natural. In contrast, our approach for the second level is based on shape descriptors which are defined by recruiting moment based representation. Finally, we show how conceptual knowledge can be utilized for visual feature definition in order to enhance recognition of subordinate categories.