T. Fischer et al., "Automatic Morphological Classification with Case-Based Reasoning", in Proc. of 7th Int. Conf. on 3D Body Scanning Technologies, Lugano, Switzerland, 2016, pp. 148-158, https://doi.org/10.15221/16.148.
Title:
Automatic Morphological Classification with Case-Based Reasoning
Authors:
Thomas FISCHER 1, Alexander ARTSCHWAGER 1, Konrad PFLEIDERER 1,
Anke RISSIEK 2, Magdalena MANDALKA 2, Andreas SEIDL 2, Rainer TRIEB 2
1 Center for Management Research, German Institutes of Textile
and Fiber Research Denkendorf, Denkendorf, Germany;
2 Human Solutions GmbH, Kaiserslautern, Germany
Abstract:
It is still a challenge for the apparel industry to develop good fitting products and underlying sizing and grading systems. This is due to the diversity of human bodies having the same traditional size but different morphotypes. Additional reasons are differences between different countries and special target groups such as young people or old people.
The objective of the iMorph-approach is the morphological classification based on body scan data to be used for size system development and to provide better fitting clothes. Additional applications include recommendations systems in online business and curated shopping.
iMorph is a unique approach to estimate the morphological classification of individuals based on body scan data. First, a morphological classification scheme was developed. It comprises 10 features and according ordinal scales. The analysis of the available body scan data showed that it is merely impossible to derive rules for the automatic morphological classification. Therefore, human experts visually classified a number of selected scans (data sets) by looking at the scanatars. A simple web-based application allows remote classification for invited experts. The resulting case base of classified scans is the core of a Case-Based Reasoning (CBR) system. It is able to compare the data of a new, unclassified scan with the scans of the case base. The most similar scans are the used derive a good estimation for the classification of the new scan.
The crucial element of this iMorph approach is the applied similarity model. The functions are specific for each morphotype feature because not all measurements are relevant for all features. An example may illustrate this: for the classification of the shoulders of a new data set, only a number of scan data related to the shoulders is relevant, for example breast girth, distance shoulder to buttock. The classification is then derived from the morphological classification of these most similar "shoulder cases".
The described approach has been proved to be valid and comprehensive. It is flexible and extendable because each classification feature has separate similarity and retrieval functions with vast expert knowledge embodied and can be linked to various case-bases. Time consuming individual morphotype classification can be replaced by CBR technology and supports fashion product development as well as size recommendation in online retail.
Details:
Full paper: 16.148.pdf
Proceedings: 3DBST 2016, 30 Nov.-1 Dec. 2016, Lugano, Switzerland
Pages: 148-158
DOI: 10.15221/16.148
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