| Abstrak/Abstract |
The popularity of East Nusa Tenggara (ENT)
province is attributed to a variety of traditional woven fabrics
with local cultural attributes. Each tribe in the province has its
design and colors that differentiate the fabrics leading to
diverse decorative motifs. Due to different varieties, it is
challenging for users to know both the type of motif and its
origins. In this research, several Convolutional neural network
(CNN) architecture benchmarks were carried out for ENT
weaving images retrieval. The image retrieval method was
chosen for the study since it has feature extraction and
similarity measurement, which make searching and selection
relatively easier. Furthermore, the CNN method is often used for
feature extraction due to its ability to recognize objects while
hashing and hamming distance algorithms help reduce the
computation time for similarity testing. This study was
conducted by comparing several pre-trained CNN
architectures, including VGG16, ResNet101, InceptionV3, and
Discrete Wavelet Transform. The results showed that the
highest accuracy is ResNet101 architecture with 100%,
88.50%, and 55% at top=1, top=5, and top=10, respectively.
The pre-trained CNN model and Discrete Wavelet Transform
combination provided better results in case the feature
dimensions were above 16-bit. The feature dimensions are
generally based on the best 6-bit hashing code, though they are
computationally time-consuming |
