Feature extraction for fashion trend detection
- 주제(키워드) computer vision , data mining , image mining , image recognition , feature extraction
- 발행기관 아주대학교
- 지도교수 Kim Dong Yoon
- 발행년도 2015
- 학위수여년월 2015. 8
- 학위명 석사
- 학과 및 전공 일반대학원 컴퓨터공학과
- 실제URI http://www.dcollection.net/handler/ajou/000000020579
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Abstract This research work is aimed to digitally study and analyze fashion and cloths’ design by mining fashion images. The ultimate goal would be detecting fashion trends and find design and fashion patterns that will help in predicting fashion and the general public reaction and the level of success or failure certain designs and collections may face. The process to reach that goal involves studying existing algorithms and approaches for image processing and computer vision, as in texture analysis, image enhancement and color theory. And studying existing work on fashion analysis and fashion recommendation engines that serves E-commerce and online shopping sites, which developed algorithms for computer vision and feature extraction which specifically targets cloth and fashion images. And finally, work with the accumulative knowledge on fashion and cloths image analysis to develop and propose my own feature set that I think can reflect and represent deigns, and help transfer the visual content to an abstract data sets, that we can apply upon it different machine learning and data mining techniques to reach our original set of goals. So the main challenge in this research is to reach the best set of features that can be extracted from a huge set images, fashion runway images, which are take in different times, conditions and using different technologies with different backgrounds and resolutions.
more목차
1. Introduction: 1
2. Related Work: 3
3. Methodology: 5
A. Collar Area Analysis: 6
B. Color Analysis: 7
1) Hue Analysis: 7
2) Saturation Analysis: 8
3) Color Value “Depth” Analysis: 9
C. Spatial Feature Analysis: 10
1) Entropy: 10
2) Vertical Symmetry: 10
4. Experimental Results: 16
A. Classification Test: 17
B. Feature Set Principle Component Analysis “PCA”: 18
C. Cluster Evaluation: 19
1) Case scenario No.1: 19
2) Case scenario No.2: 20
3) Case scenario No.3: 21
D. Feature Selection Using Step Wise Regression: 22
1) Case scenario no.1: 23
2) Case scenario No.2: 24
E. Comparison with other existing Feature Representations: 25
1) Season Classification Test: 26
2) Designer Classification Test: 27
3. Conclusion and Future Work 29
References 31
