A new U-Net based system for multi-cultural wedding image classification
Document Type
Article
Publication Date
1-1-2024
Abstract
Use of social media for communication, sharing or expressing views, broadcasting news, threatening and blackmailing has become an integral part of society. One such activity is understanding multi-cultural wedding images uploaded on social media. This paper presents a novel method based on the combination of U-Net, Convolutional Neural Network and Random Forest for classification of multicultural wedding images. In the case of wedding images, bride and bridegroom draw the attention of the viewers. This observation led to propose a U-Net for segmenting the region of bride and bridegroom in a novel way. Similarly, it is noted that the costumes of bride and bridegroom are vital information for differentiating different cultures. This cue motivated us to extract features using CNN for classification. Since the extracted features using CNN are capable of discriminating images of different classes, we propose a simple and effective Random-Forest for Multicultural Wedding Image Classification. The efficiency of the proposed model is demonstrated by testing it on our own dataset of six multi-cultural wedding classes and standard dataset of wedding and non-wedding images classes. Experimental results on both the datasets show that the proposed model outperforms the state-of-the-art models in terms of average classification rate. © 2023 Elsevier Ltd
Keywords
Classification (of information), Convolution, Image classification, Social networking (online), Statistical tests, Convolutional neural network, Images classification, Integral part, Multi-cultural wedding image, NET architecture, Random forest classifier, Random forests, Social media, U-net architecture, Wedding image, Convolutional neural networks
Divisions
fsktm
Funders
Universiti Malaya (GPF096A-2020); (GPF096B-2020); (GPF096C-2020)
Publication Title
Expert Systems with Applications
Volume
237
Publisher
Elsevier
Additional Information
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