Learning a Complete Image Indexing Pipeline
Himalaya Jain, Joaquin Zepeda, Patrick P\'erez, R\'emi Gribonval

TL;DR
This paper introduces a unified neural framework for image indexing that learns both the inverted file index and approximate distance computation, improving scalability and efficiency in large-scale image retrieval.
Contribution
It presents the first system to jointly learn both components of image indexing using structured binary encoding within a neural framework.
Findings
Achieves efficient large-scale image retrieval
Unifies index learning and distance computation
Demonstrates improved retrieval accuracy
Abstract
To work at scale, a complete image indexing system comprises two components: An inverted file index to restrict the actual search to only a subset that should contain most of the items relevant to the query; An approximate distance computation mechanism to rapidly scan these lists. While supervised deep learning has recently enabled improvements to the latter, the former continues to be based on unsupervised clustering in the literature. In this work, we propose a first system that learns both components within a unifying neural framework of structured binary encoding.
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques · Multimodal Machine Learning Applications
