Feature and Semantic Views Consensus Hashing for Image Set Classification
Yuan Sun, Dezhong Peng, Haixiao Huang, Zhenwen Ren
Abstract
Image set classification (ISC) has always been an active topic, primarily due to the fact that image set can provide more comprehensive information to describe a subject. However, the existing ISC methods face two problems: (1) The high computational cost prohibits these methods from being applied into median or large-scale applications; (2) the consensus information between feature and semantic representation of image set are largely ignored. To overcome these issues, in this paper, we propose a novel ISC method, termly feature and semantic views consensus hashing (FSVCH). Specifically, a kernelized bipartite graph is constructed to capture the nonlinear structure of data, and then two-views (\ie feature and semantic) consensus hashing learning (TCHL) is proposed to obtain a shared hidden consensus information. Meanwhile, for robust out-of-sample prediction purpose, we further propose TCHL guided optimal hash function inversion (TGHI) to learn a high-quality general hash function. Afterwards, hashing rotating (HR) is employed to obtain a more approximate real-valued hash solution. A large number of experiments show that FSVCH remarkably outperforms comparison methods on three benchmark datasets, in term of running time and classification performance. Experimental results also indicate that FSVCH can be scalable to median or large-scale ISC task.