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Fine-Grained Deep Feature Expansion Framework for Fashion Apparel Classification Using Transfer Learning
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Author(s): R. Regin (SRM Institute of Science and Technology, India), Pravin Kumar Sharma (SRM Institute of Science and Technology, India), Kunnal Singh (SRM Institute of Science and Technology, India), Y. V. Narendra (SRM Institute of Science and Technology, India), S. Rubin Bose (SRM Institute of Science and Technology, India)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
Copyright: 2024
Pages: 16
Source title:
Advanced Applications of Generative AI and Natural Language Processing Models
Source Author(s)/Editor(s): Ahmed J. Obaid (University of Kufa, Iraq), Bharat Bhushan (School of Engineering and Technology, Sharda University, India), Muthmainnah S. (Universitas Al Asyariah Mandar, Indonesia)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3693-0502-7.ch019
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Abstract
The chapter focuses on developing a deep learning-based image classification model for fashion and apparel. With the rise of online retail services, there is a growing need for accurate and efficient apps to categorize fashion garments based on their attributes from image data. The study proposes a fine-grained deep feature expansion framework using transfer learning to address this need. The dataset consists of approximately 44,000 images of fashion apparel with six categories, including gender, subcategory, article type, base color, season, and usage. The images are preprocessed to remove corrupted images and resized to 256 by 256 pixels. The proposed framework employs pre-trained CNN models such as ResNet50 or Vgg19 for feature extraction, fine-tuning, and transfer learning. The CNN architecture consists of several layers: convolutional layers, residual blocks, max-pooling layers, and dense layers.
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