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Technology Convergence (TC)

Cody Recommendation System Using Deep Learning and User Preferences

초록

영어

As AI technology is recently introduced into various fields, it is being applied to the fashion field. This paper proposes a system for recommending cody clothes suitable for a user's selected clothes. The proposed system consists of user app, cody recommendation module, and server interworking of each module and managing database data. Cody recommendation system classifies clothing images into 80 categories composed of feature combinations, selects multiple representative reference images for each category, and selects 3 full body cordy images for each representative reference image. Cody images of the representative reference image were determined by analyzing the user's preference using Google survey app. The proposed algorithm classifies categories the clothing image selected by the user into a category, recognizes the most similar image among the classification category reference images, and transmits the linked cody images to the user's app. The proposed system uses the ResNet-50 model to categorize the input image and measures similarity using ORB and HOG features to select a reference image in the category. We test the proposed algorithm in the Android app, and the result shows that the recommended system runs well.

목차

Abstract
1. INTRODUCTION
2. CONVENTIONAL METHOD
3. CODY RECOMMENDATION SYSTEM USING DEEP LEARNING AND USER PREFERENCES
3.1 The app for users.
3.2 Server and DB Configuration
3.3. Deep Learning and Video Processing for Cody Recommendation
4. EXPERIMENT AND RESULTS ANALYSIS
5. CONCLUSION
ACKNOWLEDGEMENT
REFERENCES

저자정보

  • Naejoung Kwak Instructor, Chungbuk National Univ. Dept. Information & Communication
  • Doyun Kim megaNEXT Co.Ltd
  • Minho kim megaNEXT Co.Ltd
  • Jongseo kim megaNEXT Co.Ltd
  • Sangha Myung megaNEXT Co.Ltd
  • Youngbin Yoon megaNEXT Co.Ltd
  • Jihye Choi megaNEXT Co.Ltd

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