Online Enhanced Semantic Hashing: Towards Effective And Efficient Retrieval For Streaming Multi-modal Data

Abstract

With the vigorous development of multimedia equipment and applications, efficient retrieval of large-scale multi-modal data has become a trendy research topic. Thereinto, hashing has become a prevalent choice due to its retrieval efficiency and low storage cost. Although multi-modal hashing has drawn lots of attention in recent years, there still remain some problems. The first point is that existing methods are mainly designed in batch mode and not able to efficiently handle streaming multi-modal data. The second point is that all existing online multi-modal hashing methods fail to effectively handle unseen new classes which come continuously with streaming data chunks. In this paper, we propose a new model, termed Online enhAnced SemantIc haShing (OASIS). We design novel semantic-enhanced representation for data, which could help handle the new coming classes, and thereby construct the enhanced semantic objective function. An efficient and effective discrete online optimization algor

Stats

• citations12
• S2 citations—
• github stars0
• HF likes0
• heat score8.35
• arxiv keywu2021online

Related papers

• Adaptive Asymmetric Label-guided Hashing For Multimedia Search (2022)0.00
• Towards Optimal Discrete Online Hashing With Balanced Similarity (2019)12.17
• Hadamard Matrix Guided Online Hashing (2019)14.89
• Transitive Hashing Network For Heterogeneous Multimedia Retrieval (2016)8.35
• Encode The Unseen: Predictive Video Hashing For Scalable Mid-stream Retrieval (2020)3.58
• Efficient Discrete Supervised Hashing For Large-scale Cross-modal Retrieval (2019)11.08
• Making Online Sketching Hashing Even Faster (2020)9.23
• Supervised Online Hashing Via Similarity Distribution Learning (2019)0.00