A discriminative and sparse topic model for image classification and annotation

Liu Yang, Liping Jing*, Kwok Po NG, Jian Yu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

6 Citations (Scopus)

Abstract

Image classification is to assign a category of an image and image annotation is to describe individual components of an image by using some annotation terms. These two learning tasks are strongly related. The main contribution of this paper is to propose a new discriminative and sparse topic model (DSTM) for image classification and annotation by combining visual, annotation and label information from a set of training images. The essential features of DSTM different from existing approaches are that (i) the label information is enforced in the generation of both visual words and annotation terms such that each generative latent topic corresponds to a category; (ii) the zero-mean Laplace distribution is employed to give a sparse representation of images in visual words and annotation terms such that relevant words and terms are associated with latent topics. Experimental results demonstrate that the proposed method provides the discrimination ability in classification and annotation, and its performance is better than the other testing methods (sLDA-ann, abc-corr-LDA, SupDocNADE, SAGE and MedSTC) for LabelMe, UIUC, NUS-WIDE and PascalVOC07 images.

Original languageEnglish
Pages (from-to)22-35
Number of pages14
JournalImage and Vision Computing
Volume51
DOIs
Publication statusPublished - 1 Jul 2016

Scopus Subject Areas

  • Signal Processing
  • Computer Vision and Pattern Recognition

User-Defined Keywords

  • Discriminative topic
  • Graphical model
  • Image annotation
  • Image classification
  • Sparsity

Fingerprint

Dive into the research topics of 'A discriminative and sparse topic model for image classification and annotation'. Together they form a unique fingerprint.

Cite this