fbpx

Compact and Effective Representations for Sketch-based Image Retrieval

Pablo Torres
DCC University of Chile

pablo.torres.dessi@gmail.com

Jose M. Saavedra
Impresee Inc.

 jose.saavedra@impresee.com

Abstract

Sketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce search engines because it allows users to formulate a query just by drawing what they need to buy. However, current methods showing high precision in retrieval work in a high dimensional space, which negatively affects aspects like memory consumption and time processing. Although some authors have also proposed compact representations, these drastically degrade the performance in a low dimension. Therefore in this work, we present different results of evaluating methods for producing compact embeddings in the context of sketch-based image retrieval. Our main interest is in strategies aiming to keep the local structure of the original space. The recent unsupervised local-topology preserving dimension reduction method UMAP fits our requirements and shows outstanding performance, improving even
the precision achieved by SOTA methods. We evaluate six methods in two different datasets. We use Flickr15K and eCommerce datasets; the latter is another contribution of this work. We show that UMAP allows us to have feature vectors of 16 bytes improving precision by more than 35%.

Would you like to collaborate?

Solutions

Creative Search Bar & Filters
Creative Memory

About

Our History
Partners
Pricing
Privacy Policy

Platforms

WooCommerce
Shopify
Tiendanube
Magento
Others

Resources

Blog
Tutorials
eBooks

Contact us

Schedule a demo