Semi-supervised manifold-embedded hashing with joint feature representation and classifier learning. (August 2017)

• Record Type: Journal Article
• Authors: Song, Tiecheng; Cai, Jianfei; Zhang, Tianqi; Gao, Chenqiang; Meng, Fanman; Wu, Qingbo
• Is Part Of: Pattern recognition. Volume 68(2017:Aug.)
• Page Start: 99
• Page End: 110
• Publication Date: 2017-08
• Subjects: Hashing -- Manifold embedding -- Locality sensitive hashing (LSH) -- Nearest neighbor search -- Image retrieval; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.03.004
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose a semi-supervised hashing method which uses very limited labeled data. We integrate manifold embedding, feature representation and classifier learning into a joint optimization framework. We adopt the l 2, 1 -norm in our formulation to obtain a robust model. We develop a two-stage hashing strategy to address the optimization problem. Abstract: Recently, learning-based hashing methods which are designed to preserve the semantic information, have shown promising results for approximate nearest neighbor (ANN) search problems. However, most of these methods require a large number of labeled data which are difficult to access in many real applications. With very limited labeled data available, in this paper we propose a semi-supervised hashing method by integrating manifold embedding, feature representation and classifier learning into a joint framework. Specifically, a semi-supervised manifold embedding is explored to simultaneously optimize feature representation and classifier learning to make the learned binary codes optimal for classification. A two-stage hashing strategy is proposed to effectively address the corresponding optimization problem. At the first stage, an iterative algorithm is designed to obtain a relaxed solution. At the second stage, the hashing function is refined by introducing an orthogonal transformation to reduce the quantization error. Extensive experiments on three benchmark databases demonstrate the effectiveness of the proposed method in comparison with several state-of-the-art hashing methods.