Name
Title | ||
|---|---|---|
Study on Machine Learning Algorithms to Automatically Identifying Body Type for Clothing Model Recommendation. |
Abstract | ||
|---|---|---|
The task of automatically identify body type with high accuracy is still a relevant problem in clothing fashion settings. This paper addresses such problem, presenting a study on machine learning techniques applied to classify women’s body shapes, taking into account a small set of body attributes, in order to further find appropriate clothing models. Thus, we perform a comparative study on such techniques to evaluate the accuracy of four classifiers, aiming at selecting the best of them to be used for clothing model recommendation based on rules. Overall, in the conducted computational experiment, Random Forest and SVM methods had the best performance, but the other two had also very good results, demonstrating their effectiveness to automatically identifying body type, serving as a relevant information to be used in our rule-based system to provide clothing model recommendation. |
Year | Venue | Field |
|---|---|---|
2018 | WorldCIST | Computer science,Support vector machine,Body shape,Clothing,Artificial intelligence,Random forest,Small set,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Evandro Costa | 1 | 48 | 5.32 |
| Emanuele Silva | 2 | 1 | 1.73 |
| hemilis joyse barbosa rocha | 3 | 2 | 2.44 |
| Artur Maia | 4 | 0 | 0.34 |
| Thales Vieira | 5 | 96 | 8.25 |