Multi-Deep Features Fusion Algorithm for Clothing Image Recognition

He, Zhiqiang; Li, Yuhua; Shi, Xiayang; Li, Pu; Huang, Wanwei

doi:10.1109/icdh51081.2020.00026

Cited by 1 publication

(1 citation statement)

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“…Yuan et al [2] designed and implemented a clothing matching and recommendation system based on clothing pictures and customer historical behavior data using deep learning technology and data mining to meet consumer demand and boost sales. The target detection technology and the deep residual network (ResNet) extract comprehensive clothing features, addressing the issue of interfering factors in clothing image recognition through multi-depth feature fusion [3]. This method fully leverages global, primary, and local area attributes, directing the recognition process towards the clothing itself, thereby significantly improving the accuracy rate of clothing image recognition.…”

Section: Introductionmentioning

confidence: 99%

Personalized Clothing Prediction Algorithm Based on Multi-modal Feature Fusion

Rong Liu,

Annie Anak Joseph,

Miaomiao Xin

et al. 2024

Int. j. eng. technol. innov.

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With the popularization of information technology and the improvement of material living standards, fashion consumers are faced with the daunting challenge of making informed choices from massive amounts of data. This study aims to propose deep learning technology and sales data to analyze the personalized preference characteristics of fashion consumers and predict fashion clothing categories, thus empowering consumers to make well-informed decisions. The Visuelle’s dataset includes 5,355 apparel products and 45 MB of sales data, and it encompasses image data, text attributes, and time series data. The paper proposes a novel 1DCNN-2DCNN deep convolutional neural network model for the multi-modal fusion of clothing images and sales text data. The experimental findings exhibit the remarkable performance of the proposed model, with accuracy, recall, F1 score, macro average, and weighted average metrics achieving 99.59%, 99.60%, 98.01%, 98.04%, and 98.00%, respectively. Analysis of four hybrid models highlights the superiority of this model in addressing personalized preferences.

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Section: Introductionmentioning

confidence: 99%