Classifying Objects Of Continuous Feature Variability: When Do We Stop Classifying?

Takashi Okagaki

doi:10.1117/12.968959

25 October 1988 Classifying Objects Of Continuous Feature Variability: When Do We Stop Classifying?

Takashi Okagaki

Proceedings Volume 1001, Visual Communications and Image Processing '88: Third in a Series; (1988) https://doi.org/10.1117/12.968959
Event: Visual Communications and Image Processing III, 1988, Cambridge, MA, United States

Abstract

Pattern recognition by a computer assumes that there is a correct answer in classifying the objects to which we can make reference for correctness of recognition. Classification of a set of objects may have absolutely correct answers when the objects are artifacts (e.g. bolts vs nuts) or highly evolved biological species. However, classification of many other objects is arbitrary (e.g. color, clouds), and is frequently a subject of cultural bias. For instance, traffic lights consist of red, yellow and green in the U.S.A.; they are perceived as red, yellow and blue by Japanese. When human bias is involved in classification, a natural solution is to set a panel of human "experts" and concensus of the panel is assumed to be the correct classification. For instance, expert interior decorators can define classification of different colors and hues, and performance of a machine is tested against the reference set by human experts.

Citation Download Citation

Takashi Okagaki "Classifying Objects Of Continuous Feature Variability: When Do We Stop Classifying?", Proc. SPIE 1001, Visual Communications and Image Processing '88: Third in a Series, (25 October 1988); https://doi.org/10.1117/12.968959

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