Explore-Exploit Graph Traversal for Image Retrieval

Cheng Chang, Guangwei Yu, Chundi Liu, Maksims Volkovs; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 9423-9431

Abstract


We propose a novel graph-based approach for image retrieval. Given a nearest neighbor graph produced by the global descriptor model, we traverse it by alternating between exploit and explore steps. The exploit step maximally utilizes the immediate neighborhood of each vertex, while the explore step traverses vertices that are farther away in the descriptor space. By combining these two steps we can better capture the underlying image manifold, and successfully retrieve relevant images that are visually dissimilar to the query. Our traversal algorithm is conceptually simple, has few tunable parameters and can be implemented with basic data structures. This enables fast real-time inference for previously unseen queries with minimal memory overhead. Despite relative simplicity, we show highly competitive results on multiple public benchmarks, including the largest image retrieval dataset that is currently publicly available. Full code for this work is available here: https://github.com/layer6ai-labs/egt.

Related Material


[pdf]
[bibtex]
@InProceedings{Chang_2019_CVPR,
author = {Chang, Cheng and Yu, Guangwei and Liu, Chundi and Volkovs, Maksims},
title = {Explore-Exploit Graph Traversal for Image Retrieval},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}