i-Stylist: Finding the Right Dress Through Your Social Networks

Sanchez-Riera, Jordi; Lin, Jun Ming; Hua, Kai; Cheng, Wen-Huang; Tsui, Arvin Wen

doi:10.1007/978-3-319-51811-4_54

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…For building the bridge between fashion compatibility and personalized preference in outfit matching tasks, there are a few methods for this goal. A personalized clothing recommendation system, namely i-Stylist that retrieved clothing items through the analysis of user's images, was developed in Reference [155]. The i-Stylist organized the deep learning features and clothing properties of user's clothing items as a fully connected graph.…”

Section: State-of-the-art Methodsmentioning

confidence: 99%

Fashion Meets Computer Vision

et al. 2021

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Fashion is the way we present ourselves to the world and has become one of the world’s largest industries. Fashion, mainly conveyed by vision, has thus attracted much attention from computer vision researchers in recent years. Given the rapid development, this article provides a comprehensive survey of more than 200 major fashion-related works covering four main aspects for enabling intelligent fashion: (1) Fashion detection includes landmark detection, fashion parsing, and item retrieval; (2) Fashion analysis contains attribute recognition, style learning, and popularity prediction; (3) Fashion synthesis involves style transfer, pose transformation, and physical simulation; and (4) Fashion recommendation comprises fashion compatibility, outfit matching, and hairstyle suggestion. For each task, the benchmark datasets and the evaluation protocols are summarized. Furthermore, we highlight promising directions for future research.

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Section: State-of-the-art Methodsmentioning

confidence: 99%

Fashion Meets Computer Vision

et al. 2021

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“…Based on classifying apparel into distinct categories of shoes, clothing, accessories in addition to their colors, styles, fabrics and patterns, recommendations and feedback for clothing have been discussed in [15,16]. Kalantidis et al [16] have considered an outdated version of clothing feedback that had misclassification.…”

Section: Detecting Colors and Clothing Patterns Using Machine Learning Techniquesmentioning

confidence: 99%

“…Kalantidis et al [16] have considered an outdated version of clothing feedback that had misclassification. Thus, i-Stylist has been proposed to enable clothing recommendations to the users based on their styles from their social images, and the recommendations can be enhanced through users' feedback [15]. The core of i-Stylist model is based on merging state-of-the-art fine-tuned CNN features besides the acquired metadata from SVM models.…”

Section: Detecting Colors and Clothing Patterns Using Machine Learning Techniquesmentioning

confidence: 99%

Recognizing Clothing Patterns and Colors for BVI People Using Different Techniques

Allam¹,

Elshaarawy

Farghal

2021

Digital Transformation Technology

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For blind and visually impaired (BVI) people, it is an arduous task to recognize clothing patterns and colors. It is especially complex to recognize them automatically. This is a highly researched area in image processing. This paper provides BVI with the ability to detect patterns and colors without depending on personal assistance, leading to increasing their confidence. The user first captures an image; then, our system detects colors and patterns. The color model used here is the hue saturation value (HSV). The main colors detected by our system are magenta, cyan, blue, green, red and yellow. The saturation of each color is also taken into consideration. We propose various methods that include multiple feature extraction and classification techniques on the City College of New York (CCNY) clothing pattern dataset. The combinations of the feature extraction and classification techniques exceeded 200 methods. The proposed methods were performed using MATLAB and Weka under different preprocessing and compression methods, such as converting the dataset to grayscale, using raw red, green and blue (RGB) images, in addition to grayscale and RGB scalograms from continuous wavelets. Using a combination of histogram of oriented gradients (HOG), feature extraction and a sequential minimal optimization (SMO) classifier resulted in the highest recognition rate, achieving 91.8% accuracy.

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“…For building the bridge between fashion compatibility and personalized preference in outfit matching tasks, there are a few methods for this goal. A personalized clothing recommendation system, namely i-Stylist that retrieved clothing items through the analysis of user's images, was developed in [147]. for users, Hou et al [62] proposed a semantic attribute explainable recommender system to not only recommend for personalized compatible items but also explain the reason why the system recommends it.…”

Section: 21mentioning

confidence: 99%

Fashion Meets Computer Vision: A Survey

Cheng¹,

Song²,

Chen³

et al. 2020

Preprint

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Fashion is the way we present ourselves to the world and has become one of the world's largest industries. Fashion, mainly conveyed by vision, has thus attracted much attention from computer vision researchers in recent years. Given the rapid development, this paper provides a comprehensive survey of more than 200 major fashion-related works covering four main aspects for enabling intelligent fashion: (1) Fashion detection includes landmark detection, fashion parsing, and item retrieval, (2) Fashion analysis contains attribute recognition, style learning, and popularity prediction, (3) Fashion synthesis involves style transfer, pose transformation, and physical simulation, and (4) Fashion recommendation comprises fashion compatibility, outfit matching, and hairstyle suggestion. For each task, the benchmark datasets and the evaluation protocols are summarized. Furthermore, we highlight promising directions for future research.

show abstract

i-Stylist: Finding the Right Dress Through Your Social Networks

Cited by 8 publications

References 18 publications

Fashion Meets Computer Vision

Fashion Meets Computer Vision

Recognizing Clothing Patterns and Colors for BVI People Using Different Techniques

Fashion Meets Computer Vision: A Survey

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