Paper Info
Title
Efficient Multi-Attribute Similarity Learning Towards Attribute-Based Fashion Search
Abstract
In this paper, we propose an attribute-based query & retrieval system designed for fashion products. Our system addresses the problem of carrying out fashion searches by the query image and attribute manipulation, e.g. replacing long sleeve attribute of a dress to sleeveless. We present the attributes in two groups: (1) general attributes (category, gender etc.) and (2) special attributes (sleeve length, collar etc.). The special attributes are more suitable for the attribute manipulation and thus conducting searches. In order to solve the mentioned fashion search problem, it is crucial for the deep neural networks to understand attribute similarities. To facilitate more specific similarity learning, clothing items are represented by their structural sub-components or "parts". The parts are estimated using an unsupervised segmentation method and used inside the proposed Convolutional Neural Network (CNN) as an attention mechanism. Meaning, different parts are connected to the special attributes, e.g. sleeve part is connected with sleeve length attribute. With this mechanism, part-based triplet ranking constraint is applied to learn similarity of each special attribute independently from one another in a single network. In the end, the well-defined features are used to conduct the fashion search. Additionally, an adaptive relevance feedback module is used to personalize the fashion search process with the feature descriptions. For our experiments, a new dataset is constructed containing 101,021 images which consist of pure clothing items. Besides achieving decent retrieval results in our dataset, the experiments show that proposed technique outperforms different baselines and is able to adapt towards user's requests.
Year
DOI
Venue
2018
10.1109/WACV.2018.00186
2018 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2018)
Field
DocType
ISSN
Similarity learning,Relevance feedback,Ranking,Information retrieval,Pattern recognition,Segmentation,Convolutional neural network,Computer science,Image retrieval,Feature extraction,Artificial intelligence,Search problem
Conference
2472-6737
Citations 
PageRank 
References 
4
0.37
0
Authors
4
Name
Order
Citations
PageRank
Ak, Kenan E.1142.52
Joo-Hwee Lim278382.45
Jo Yew Tham344247.35
Ashraf A. Kassim4116497.26