Online Supervised Hashing for Ever-Growing Datasets

TL;DR

This paper introduces an online supervised hashing algorithm designed to efficiently handle growing datasets in computer vision, maintaining semantic accuracy and reducing update costs compared to batch methods.

Contribution

The paper presents a novel online supervised hashing approach that adapts to dataset growth, offering linear complexity and a framework to minimize hash table updates.

Findings

Significant improvements over state-of-the-art methods on benchmark datasets.

Efficient handling of dataset expansion with reduced hash table updates.

Maintains semantic neighborhood preservation in an online setting.

Abstract

Supervised hashing methods are widely-used for nearest neighbor search in computer vision applications. Most state-of-the-art supervised hashing approaches employ batch-learners. Unfortunately, batch-learning strategies can be inefficient when confronted with large training datasets. Moreover, with batch-learners, it is unclear how to adapt the hash functions as a dataset continues to grow and diversify over time. Yet, in many practical scenarios the dataset grows and diversifies; thus, both the hash functions and the indexing must swiftly accommodate these changes. To address these issues, we propose an online hashing method that is amenable to changes and expansions of the datasets. Since it is an online algorithm, our approach offers linear complexity with the dataset size. Our solution is supervised, in that we incorporate available label information to preserve the semantic neighborhood. Such an adaptive hashing method is attractive; but it requires recomputing the hash table as the hash functions are updated. If the frequency of update is high, then recomputing the hash table entries may cause inefficiencies in the system, especially for large indexes. Thus, we also propose a framework to reduce hash table updates. We compare our method to state-of-the-art solutions on two benchmarks and demonstrate significant improvements over previous work.