Semi-supervised semantic factorization hashing for fast cross-modal retrieval

Abstract

Cross-modal hashing can effectively solve the large-scale cross-modal retrieval by integrating the advantages of traditional cross-modal analysis and hashing techniques. In cross-modal hashing, preserving semantic correlation is important and challenging. However, current hashing methods cannot well preserve the semantic correlation in hash codes. Supervised hashing requires labeled data which is difficult to obtain, and unsupervised hashing cannot effectively learn semantic correlation from multi-modal data. In order to effectively learn semantic correlation to improve hashing performance, we propose a novel approach: Semi-Supervised Semantic Factorization Hashing (S3FH), for large-scale cross-modal retrieval. The main purpose of S3FH is to improve semantic labels and factorize it into hash codes. It optimizes a joint framework which consists of three interactive parts, including semantic factorization, multi-graph learning and multi-modal correlation. Then, an efficient alternating algorithm is derived for optimizing S3FH. Extensive experiments on two real world multi-modal datasets demonstrate the effectiveness of S3FH.