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Techniques to learn hash codes which can store and retrieve large dimensionalmultimedia data efficiently have attracted broad research interests in therecent years. With rapid explosion of newly emerged concepts and online data,existing supervised hashing algorithms suffer from the problem of scarcity ofground truth annotations due to the high cost of obtaining manual annotations.Therefore, we propose an algorithm to learn a hash function from trainingimages belonging to `seen' classes which can efficiently encode images of`unseen' classes to binary codes. Specifically, we project the image featuresfrom visual space and semantic features from semantic space into a commonHamming subspace. Earlier works to generate hash codes have tried to relax thediscrete constraints on hash codes and solve the continuous optimizationproblem. However, it often leads to quantization errors. In this work, we usethe max-margin classifier to learn an efficient hash function. To address theconcern of domain-shift which may arise due to the introduction of new classes,we also introduce an unsupervised domain adaptation model in the proposedhashing framework. Results on the three datasets show the advantage of usingdomain adaptation in learning a high-quality hash function and superiority ofour method for the task of image retrieval performance as compared to severalstate-of-the-art hashing methods.

Hashing in the Zero Shot Framework with Domain Adaptation