Feature-based sparse representation for image similarity assessment

Li Wei Kang*, Chao Yung Hsu, Hung Wei Chen, Chun Shien Lu, Chih Yang Lin, Soo Chang Pei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

58 Citations (Scopus)

Abstract

Assessment of image similarity is fundamentally important to numerous multimedia applications. The goal of similarity assessment is to automatically assess the similarities among images in a perceptually consistent manner. In this paper, we interpret the image similarity assessment problem as an information fidelity problem. More specifically, we propose a feature-based approach to quantify the information that is present in a reference image and how much of this information can be extracted from a test image to assess the similarity between the two images. Here, we extract the feature points and their descriptors from an image, followed by learning the dictionary/basis for the descriptors in order to interpret the information present in this image. Then, we formulate the problem of the image similarity assessment in terms of sparse representation. To evaluate the applicability of the proposed feature-based sparse representation for image similarity assessment (FSRISA) technique, we apply FSRISA to three popular applications, namely, image copy detection, retrieval, and recognition by properly formulating them to sparse representation problems. Promising results have been obtained through simulations conducted on several public datasets, including the Stirmark benchmark, Corel-1000, COIL-20, COIL-100, and Caltech-101 datasets.

Original languageEnglish
Article number5872049
Pages (from-to)1019-1030
Number of pages12
JournalIEEE Transactions on Multimedia
Volume13
Issue number5
DOIs
Publication statusPublished - 2011 Oct
Externally publishedYes

Keywords

  • Feature detection
  • image copy detection
  • image recognition
  • image retrieval
  • image similarity assessment
  • sparse representation

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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