Efficient Multi-Attribute Similarity Learning Towards Attribute-based Fashion Search

Efficient Multi-Attribute Similarity Learning Towards Attribute-based Fashion Search
Title:
Efficient Multi-Attribute Similarity Learning Towards Attribute-based Fashion Search
Other Titles:
2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
DOI:
10.1109/WACV.2018.00186
Publication Date:
12 March 2018
Citation:
K. E. Ak, J. H. Lim, J. Y. Tham and A. A. Kassim, "Efficient Multi-attribute Similarity Learning Towards Attribute-Based Fashion Search," 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 2018, pp. 1671-1679.
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 subcomponents 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.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2018 IEEE.
ISBN:
978-1-5386-4886-5
978-1-5386-4887-2
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