Supervised and unsupervised parallel subspace learning for large-scale image recognition

Xiao Yuan Jing, Sheng Li, Dapeng Zhang, Jian Yang, Jing Yu Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)

Abstract

Subspace learning is an effective and widely used image feature extraction and classification technique. However, for the large-scale image recognition issue in real-world applications, many subspace learning methods often suffer from large computational burden. In order to reduce the computational time and improve the recognition performance of subspace learning technique under this situation, we introduce the idea of parallel computing which can reduce the time complexity by splitting the original task into several subtasks. We develop a parallel subspace learning framework. In this framework, we first divide the sample set into several subsets by designing two random data division strategies that are equal data division and unequal data division. These two strategies correspond to equal and unequal computational abilities of nodes under parallel computing environment. Next, we calculate projection vectors from each subset in parallel. The graph embedding technique is employed to provide a general formulation for parallel feature extraction. After combining the extracted features from all nodes, we present a unified criterion to select most distinctive features for classification. Under the developed framework, we separately propose supervised and unsupervised parallel subspace learning approaches, which are called parallel linear discriminant analysis (PLDA) and parallel locality preserving projection (PLPP). PLDA selects features with the largest Fisher scores by estimating the weighted and unweighted sample scatter, while PLPP selects features with the smallest Laplacian scores by constructing a whole affinity matrix. Theoretically, we analyze the time complexities of proposed approaches and provide the fundamental supports for applying random division strategies. In the experiments, we establish two real parallel computing environments and employ four public image and video databases as the test data. Experimental results demonstrate that the proposed approaches outperform several related supervised and uns
Original languageEnglish
Article number6209404
Pages (from-to)1497-1511
Number of pages15
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume22
Issue number10
DOIs
Publication statusPublished - 9 Oct 2012

Keywords

  • Feature selection
  • graph embedding
  • large-scale image recognition
  • parallel linear discriminant analysis (PLDA)
  • parallel locality preserving projection (PLPP)
  • parallel subspace learning framework

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering

Cite this