Deep Hashing via Householder Quantization
Lucas R. Schwengber, Lucas Resende, Paulo Orenstein, Roberto I., Oliveira

TL;DR
This paper introduces a novel two-stage deep hashing method using Householder transformations that improves quantization without sacrificing similarity performance, achieving state-of-the-art results in image retrieval.
Contribution
It proposes a new quantization approach with orthogonal transformations, enhancing existing deep hashing algorithms without additional hyperparameters or performance loss.
Findings
Achieves state-of-the-art image retrieval performance.
Provides consistent improvements over existing deep hashing methods.
Efficiently leverages Householder matrices for orthogonal transformations.
Abstract
Hashing is at the heart of large-scale image similarity search, and recent methods have been substantially improved through deep learning techniques. Such algorithms typically learn continuous embeddings of the data. To avoid a subsequent costly binarization step, a common solution is to employ loss functions that combine a similarity learning term (to ensure similar images are grouped to nearby embeddings) and a quantization penalty term (to ensure that the embedding entries are close to binarized entries, e.g., -1 or 1). Still, the interaction between these two terms can make learning harder and the embeddings worse. We propose an alternative quantization strategy that decomposes the learning problem in two stages: first, perform similarity learning over the embedding space with no quantization; second, find an optimal orthogonal transformation of the embeddings so each coordinate of…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Remote-Sensing Image Classification · Video Surveillance and Tracking Methods
