Semi-supervised Hashing for Semi-Paired Cross-View Retrieval
This addresses a practical limitation in real-world retrieval systems where fully-paired data is often unavailable, though it appears incremental as it builds on existing hashing techniques.
The paper tackles the problem of cross-view retrieval when data are not fully paired, proposing a semi-supervised hashing method that jointly performs feature extraction and classifier learning. Experimental results on two datasets show it outperforms state-of-the-art methods in retrieval accuracy.
Recently, hashing techniques have gained importance in large-scale retrieval tasks because of their retrieval speed. Most of the existing cross-view frameworks assume that data are well paired. However, the fully-paired multiview situation is not universal in real applications. The aim of the method proposed in this paper is to learn the hashing function for semi-paired cross-view retrieval tasks. To utilize the label information of partial data, we propose a semi-supervised hashing learning framework which jointly performs feature extraction and classifier learning. The experimental results on two datasets show that our method outperforms several state-of-the-art methods in terms of retrieval accuracy.