Sketch Me That Shoe

• Published in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 799 - 807
• Main Authors: Qian Yu, Feng Liu, Yi-Zhe Song, Tao Xiang, Hospedales, Timothy M., Loy, Chen Change
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-06-2016
• Subjects: Data models; Footwear; Image retrieval; Machine learning; Training; Training data; Visualization
• ISSN: 1063-6919
• DOI: 10.1109/CVPR.2016.93

We investigate the problem of fine-grained sketch-based image retrieval (SBIR), where free-hand human sketches are used as queries to perform instance-level retrieval of images. This is an extremely challenging task because (i) visual comparisons not only need to be fine-grained but also executed cross-domain, (ii) free-hand (finger) sketches are highly abstract, making fine-grained matching harder, and most importantly (iii) annotated cross-domain sketch-photo datasets required for training are scarce, challenging many state-of-the-art machine learning techniques. In this paper, for the first time, we address all these challenges, providing a step towards the capabilities that would underpin a commercial sketch-based image retrieval application. We introduce a new database of 1,432 sketchphoto pairs from two categories with 32,000 fine-grained triplet ranking annotations. We then develop a deep tripletranking model for instance-level SBIR with a novel data augmentation and staged pre-training strategy to alleviate the issue of insufficient fine-grained training data. Extensive experiments are carried out to contribute a variety of insights into the challenges of data sufficiency and over-fitting avoidance when training deep networks for finegrained cross-domain ranking tasks.