Hashing in the Zero Shot Framework with Domain Adaptation
Shubham Pachori, Ameya Deshpande, Shanmuganathan Raman

TL;DR
This paper introduces a zero-shot hashing framework with domain adaptation that effectively encodes unseen class images into binary codes, improving retrieval performance by addressing domain shift and quantization errors.
Contribution
It proposes a novel hashing algorithm that combines max-margin classifiers with unsupervised domain adaptation for zero-shot image retrieval.
Findings
Domain adaptation improves hash function quality.
The method outperforms state-of-the-art hashing techniques.
Effective encoding of unseen classes demonstrated.
Abstract
Techniques to learn hash codes which can store and retrieve large dimensional multimedia data efficiently have attracted broad research interests in the recent years. With rapid explosion of newly emerged concepts and online data, existing supervised hashing algorithms suffer from the problem of scarcity of ground truth annotations due to the high cost of obtaining manual annotations. Therefore, we propose an algorithm to learn a hash function from training images belonging to `seen' classes which can efficiently encode images of `unseen' classes to binary codes. Specifically, we project the image features from visual space and semantic features from semantic space into a common Hamming subspace. Earlier works to generate hash codes have tried to relax the discrete constraints on hash codes and solve the continuous optimization problem. However, it often leads to quantization errors. In…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Video Surveillance and Tracking Methods · Multimodal Machine Learning Applications
