Clothing Image Recognition Based on Multiple Features Using Deep Neural Networks

Shubathra, S; Kalaivaani, Pcd; Santhoshkumar, S.

doi:10.1109/icesc48915.2020.9155959

Cited by 17 publications

(8 citation statements)

References 9 publications

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“…Compared to the other equivalent systems loss values are less in this system. In [7] clothing prediction & classification system is developed with MLP, CNN & extreme learning machines and the data used is Fashion MNIST and the implementation results were with impressive accuracy.…”

Section: Review Of Literaturementioning

confidence: 99%

Fashion Recommender System (FRS): Image Based Engine for Personalized Outfit

Sarojadevi¹,

Bhat²,

Palekar³

et al. 2023

AJCST

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The methods used to predict how highly a person will evaluate a product or a group of people are known as recommendation systems. Books, movies, restaurants, and other products can be among the things commonly recommended. Objects where people differ in their preferences matter a lot. For predicting the preferences 2 methods are used, one a content based approach that considers the characteristic of an item, and the other is a collaborative method that evaluates choices by taking into account previous user behaviour. In this paper, a system for recommending fashion items is proposed, one that will base its recommendations on the provided clothing images' styles. The upper body and lower body clothing images, as well as those of a human model, are the main focus of this work. We have used the fashion image dataset from Kaggle website. This paper presents an idea to develop a content based recommendation system that uses convolutional neural network model, ResNet-50.

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Section: Review Of Literaturementioning

confidence: 99%

Fashion Recommender System (FRS): Image Based Engine for Personalized Outfit

Sarojadevi¹,

Bhat²,

Palekar³

et al. 2023

AJCST

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“…Convolutional Neural Network (CNN) adalah algoritma Deep Learning yang dapat menangkap input image atau gambar, menetapkan signifikansi (bobot yang dapat dipelajari) ke dalam aspek atau objek yang berbeda pada gambar, dan memisahkannya satu sama lain [14]. Struktur pada convolutional network (ConvNet) mirip dengan pola komunikasi neuron otak manusia, sebagaimana juga yang dibahas dalam penelitian [8], yaitu pengertian Artificial neural network adalah sistem komputer yang secara samar-samar menyerupai cara kerja otak manusia.…”

Section: Convolutional Neural Networkunclassified

Identifikasi Foto Fashion Dengan Menggunakan Convolutional Neural Network (CNN)

Sandag

Waworundeng

2021

CogITo Smart Journal

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Perkembangan teknologi sekarang ini berdampak pada banyak hal, salah satunya ialah pada bidang fashion. Penggunaan Artificial Intelligence dan juga deep learning dapat dimanfaatkan dalam bidang fashion, salah satu contohnya adalah pengenalan objek clothing. Pada penelitian ini, peneliti mengidentifikasi mode pakaian dengan menggunakan metode Convolutional Neural Network (CNN), dan library Tensorflow, serta menggunakan Fashion MNIST dataset untuk menguji kemampuan CNN model. Hasil yang didapatkan saat pengujian dengan menggunakan berbagai convolutional layer sekuensial yang kompleks, didapati dua hasil yang sedikit berbeda. Pengujian pada model pertama, terjadi overfitting, sehingga menghasilkan akurasi sebesar 91%. Pada pengujian kedua, dengan penambahan Dropout layers, menghasilkan akurasi yang lebih baik, yaitu sebesar 93%. Melihat dari hasil yang didapatkan, penggunaan CNN dalam mengidentifikasi mode pakaian cukup sesuai karena dapat mencapai akurasi hingga 93%. Kata kunci — Deep Learning, Pengenalan objek , Convolutional Neural Network (CNN), Tensorflow, Fashion MNIST

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“…The use of neural network-based methods can efficiently address this problem [4] . By inputting a large quantity of clothes images into a neural network and training it to automatically classify different styles of clothing, clothing materials can be classified quickly and accurately [5][6] .…”

Section: Introductionmentioning

confidence: 99%

Neural Network Models in Fashion Design Recommendation with Interactive Visualization Methods

Zhao¹,

Zou²

2023

Preprint

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As people demand for personalized clothing continues to grow, the application prospects of visual interactive design software in the clothing industry are very promising. But the traditional design process depends a lot on the work of designers, and only experienced designers can think about how style and pattern affect the overall look of clothing. As an artificial intelligence technology that has been around for a while, neural networks can be used to help with design by suggesting materials for clothes. This paper suggests a way to recommend clothing materials to designers using clothing design software by combining interactive visualization and neural network models. The method utilizes the progress and content information of the designer's current project and extracts features through a convolutional neural network (CNN) to recommend the design materials that may be needed next. The experimental results show that this method can not only provide better material recommendations for designers but also improve design efficiency and shorten the design time. Based on the Polyvore dataset, the test results show that when using this method, designers chose materials at the top of the recommendation 67% of the time, reduced the time between material selections by 43%, and reduced the average number of completed steps in the project by 56%. This method performs well in many aspects and can be integrated into clothing design software as an effective tool for clothing designers.

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Clothing Image Recognition Based on Multiple Features Using Deep Neural Networks

Cited by 17 publications

References 9 publications

Fashion Recommender System (FRS): Image Based Engine for Personalized Outfit

Fashion Recommender System (FRS): Image Based Engine for Personalized Outfit

Identifikasi Foto Fashion Dengan Menggunakan Convolutional Neural Network (CNN)

Neural Network Models in Fashion Design Recommendation with Interactive Visualization Methods

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