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

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 Fine-Grained Image Retrieval: the Text/Sketch Input Dilemma

2017 ◽  
Author(s):  
Jifei Song ◽  
Yi-zhe Song ◽  
Tony Xiang ◽  
Timothy Hospedales
Keyword(s):  
Image Retrieval ◽  
Fine Grained

scholarly journals Multiproxies Adaptive Distribution Loss with Weakly Supervised Feature Aggregation for Fine-Grained Retrieval

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hongwei Zhao ◽  
Danyang Zhang ◽  
Jiaxin Wu ◽  
Pingping Liu
Keyword(s):  
Image Retrieval ◽  
Image Feature ◽  
Cluster Center ◽  
Unified Framework ◽  
Retrieval Method ◽  
Fine Grained ◽  
Feature Aggregation ◽  
Benchmark Datasets ◽  
Weakly Supervised ◽  
Representative Image

Fine-grained retrieval is one of the complex problems in computer vision. Compared with general content-based image retrieval, fine-grained image retrieval faces more difficult challenges. In fine-grained image retrieval tasks, all classes belong to a subclass of a meta-class, so there will be small interclass variance and large intraclass variance. In order to solve this problem, in this paper, we propose a fine-grained retrieval method to improve loss and feature aggregation, which can achieve better retrieval results under a unified framework. Firstly, we propose a novel multiproxies adaptive distribution loss which can better characterize the intraclass variations and the degree of dispersion of each cluster center. Secondly, we propose a weakly supervised feature aggregation method based on channel weighting, which distinguishes the importance of different feature channels to obtain more representative image feature descriptors. We verify the performance of our proposed method on the universal benchmark datasets such as CUB200-2011 and Stanford Dog. Higher Recall@K demonstrates the advantage of our proposed method over the state of the art.

scholarly journals More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval

2021 ◽  
Author(s):  
Ayan Kumar Bhunia ◽  
Pinaki Nath Chowdhury ◽  
Aneeshan Sain ◽  
Yongxin Yang ◽  
Tao Xiang ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Supervised Learning ◽  
Fine Grained

scholarly journals Fine-Grained Color Sketch-Based Image Retrieval

2019 ◽  
pp. 424-430 ◽  
Author(s):  
Yu Xia ◽  
Shuangbu Wang ◽  
Yanran Li ◽  
Lihua You ◽  
Xiaosong Yang ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Fine Grained

Sub-Region Localized Hashing for Fine-Grained Image Retrieval

2022 ◽  
Vol 31 ◽  
pp. 314-326
Author(s):  
Xinguang Xiang ◽  
Yajie Zhang ◽  
Lu Jin ◽  
Zechao Li ◽  
Jinhui Tang
Keyword(s):  
Image Retrieval ◽  
Fine Grained
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