Paper Info
Title
Deep embedding of concept ontology for hierarchical fashion recognition
Abstract
The natural concept ontology structure of clothes has enabled easy management of large quantities of fashion images for online retailers and it is meaningful to study how to automatically recognize fashion images for both commercial promotion and academic research. In this paper, a new hierarchical approach is developed for large-scale fashion recognition. We first embed concept ontology into deep convolutional neural network (CNN) by adopting multiple deep CNN branches to learn node-specific features and classifiers explicitly. Then, we introduce a hierarchical knowledge distillation method to further improve the performance of fashion recognition. Finally, we employ the proposed approach for fashion recommendation. To deal with hierarchical deep learning constrains, we leverage back propagation to simultaneously refine the shared deep CNNs and the diverse CNN branches for relevant node features and classifiers by using our joint objective function. The main advantages of this paper lie in (1) providing an effective way for recognizing rich semantic explanations of fashion images without training large or multiple networks, and (2) saving the storage&time costs by learning personalized features and classifiers for each tree node. The experimental results on both our organized fashion dataset and the public DeepFashion dataset have verified the effectiveness and efficiency of the proposed approach on both hierarchical fashion recognition and within category fine-grained fashion recommendation.
Year
DOI
Venue
2021
10.1016/j.neucom.2020.04.085
Neurocomputing
Keywords
DocType
Volume
Hierarchical fashion recognition,Hierarchical deep feature,Hierarchical deep classifier,Hierarchical distillation
Journal
425
ISSN
Citations 
PageRank 
0925-2312
1
0.35
References 
Authors
0
5
Name
Order
Citations
PageRank
Zhenzhong Kuang16211.86
Xin Zhang211.36
Jun Yu32597105.69
Zongmin Li45411.61
Jianping Fan52677192.33