Discriminative Supervised Hashing for Cross-Modal similarity Search
This work addresses cross-modal similarity search for applications like multimedia retrieval, but it appears incremental as it builds on existing hashing techniques with hybrid improvements.
The paper tackles the problem of inadequate discriminative features in cross-modal hashing for similarity search, resulting in lower accuracy and robustness, and proposes a novel framework that outperforms state-of-the-art methods on three public datasets.
With the advantage of low storage cost and high retrieval efficiency, hashing techniques have recently been an emerging topic in cross-modal similarity search. As multiple modal data reflect similar semantic content, many researches aim at learning unified binary codes. However, discriminative hashing features learned by these methods are not adequate. This results in lower accuracy and robustness. We propose a novel hashing learning framework which jointly performs classifier learning, subspace learning and matrix factorization to preserve class-specific semantic content, termed Discriminative Supervised Hashing (DSH), to learn the discrimative unified binary codes for multi-modal data. Besides, reducing the loss of information and preserving the non-linear structure of data, DSH non-linearly projects different modalities into the common space in which the similarity among heterogeneous data points can be measured. Extensive experiments conducted on the three publicly available datasets demonstrate that the framework proposed in this paper outperforms several state-of -the-art methods.