TY  - JOUR
TI  - Analysis and classification of footwear line drawings: research on fashion attributes using computer vision algorithms
AU  - Jingjing, Li
AU  - Yebao, Zhao
AU  - Hou, Keyu
AU  - Jin, Zhou
T2  - Industria Textila
AB  - With the rapid evolution of fashion trends and consumer preferences, the imperative for agility in footwear design has
become increasingly pronounced. Central to the design process was the criticality of shoe line drawings, the burgeoning
advancements in computer vision and deep learning technologies have engendered a wealth of research in fashion
element recognition. Regrettably, the application of such advancements to footwear remains relatively underexplored.
This study introduces a novel computer vision system tailored to discern and categorise footwear line drawings. The
methodology entails the preliminary training of Mask R-CNN for shoe body extraction from footwear imagery, followed
by applying the PIDINet edge detection algorithm for line drawing delineation, culminating in utilising a classification
model for line drawing. Encouragingly, our findings evince the system’s adeptness in successful line drawing extraction
and classification, particularly demonstrating heightened accuracy in differentiating distinct styles such as nude shoes,
boots, and slippers characterized by salient outline features. This pioneering endeavour not only addresses a gap in
footwear element recognition research but also circumvents the need for an extensive footwear database for algorithmic
training. The anticipated automation of algorithmic footwear line drawing recognition holds promise for enhancing
operational efficiency and innovation, fostering sustainable advancements in fashion research.
DA  - 2024/12/20/
PY  - 2024
DO  - 10.35530/IT.075.06.2023127
DP  - DOI.org (Crossref)
VL  - 75
IS  - 06
SP  - 760
EP  - 767
SN  - 12225347
ST  - Analysis and classification of footwear line drawings
UR  - https://www.revistaindustriatextila.ro/images/2024/6/011%20LI%20JINGJING%20INDUSTRIA%20TEXTILA%20no.6_2024.pdf
Y2  - 2024/12/28/15:30:03
ER  -