Towards effective codebookless model for image classification

Qilong Wang, Peihua Li, Lei Zhang, Wangmeng Zuo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

26 Citations (Scopus)

Abstract

Different from the widely used BoF methods which model images with a pre-trained codebook, the alternative codebook-free image modeling method, which we call codebookless model (CLM), attracts little attention. In this paper, we present an effective CLM that represents an image with a single Gaussian for classification. By embedding Gaussian manifold into a vector space, we show that the simple incorporation of our CLM into a linear classifier achieves very competitive accuracy compared with state-of-the-art BoF methods (e.g., Fisher Vector). Since our CLM lies in a high-dimensional Riemannian manifold, we further propose a joint learning method of low-rank transformation with support vector machine (SVM) classifier on the Gaussian manifold, in order to reduce computational and storage cost. To study and alleviate the side effect of background clutter on our CLM, we also present a simple yet effective partial background removal method based on saliency detection. Experiments are extensively conducted on eight widely used databases to demonstrate the effectiveness and efficiency of our CLM method.
Original languageEnglish
Pages (from-to)63-71
Number of pages9
JournalPattern Recognition
Volume59
DOIs
Publication statusPublished - 1 Nov 2016

Keywords

  • Bag-of-features
  • Codebookless model
  • Image classification
  • Riemannian manifold

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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