scholarly journals Cross-domain Generative Learning for Fine-Grained Sketch-Based Image Retrieval

2017 ◽  
Author(s):  
Kaiyue Pang ◽  
Yi-zhe Song ◽  
Tony Xiang ◽  
Timothy Hospedales
Keyword(s):  
Image Retrieval ◽  
Generative Learning ◽  
Fine Grained ◽  
Cross Domain

scholarly journals A New Algorithm for Sketch-Based Fashion Image Retrieval Based on Cross-Domain Transformation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Haopeng Lei ◽  
Simin Chen ◽  
Mingwen Wang ◽  
Xiangjian He ◽  
Wenjing Jia ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Online Shopping ◽  
Unsolved Problem ◽  
Correct Match ◽  
Retrieval Accuracy ◽  
Fine Grained ◽  
Cross Domain ◽  
Domain Transformation ◽  
Domain Similarity ◽  
Natural Way

Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long-standing unsolved problem for users to find the interested products quickly. Different from the traditional text-based and exemplar-based image retrieval techniques, sketch-based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross-domain discrepancy between the free-hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch-based fashion image retrieval based on cross-domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch-photo pairs. Thus, we contribute a fine-grained sketch-based fashion image retrieval dataset, which includes 36,074 sketch-photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top-1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine-grained instance-level datasets, i.e., QMUL-shoes and QMUL-chairs, show that our model has achieved a better performance than other existing methods.

scholarly journals Sketch Less for More: On-the-Fly Fine-Grained Sketch-Based Image Retrieval

2020 ◽  
Author(s):  
Ayan Kumar Bhunia ◽  
Yongxin Yang ◽  
Timothy M. Hospedales ◽  
Tao Xiang ◽  
Yi-Zhe Song
Keyword(s):  
Image Retrieval ◽  
Fine Grained

scholarly journals Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval

2017 ◽  
Vol 26 (12) ◽  
pp. 5908-5921 ◽  
Author(s):  
Ke Li ◽  
Kaiyue Pang ◽  
Yi-Zhe Song ◽  
Timothy M. Hospedales ◽  
Tao Xiang ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Fine Grained ◽  
Subspace Alignment
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