Content-based image retrieval via a hierarchical-local-feature extraction scheme

Muwei Jian, Yilong Yin, Junyu Dong, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

23 Citations (Scopus)

Abstract

Recently, with the development of various camera sensors and internet network, the volume of digital images is becoming big. Content-based image retrieval (CBIR), especially in network big data analysis, has attracted wide attention. CBIR systems normally search the most similar images to the given query example among a wide range of candidate images. However, human psychology suggests that users concern more about regions of their interest and merely want to retrieve images containing relevant regions, while ignoring irrelevant image areas (such as the texture regions or background). Previous CBIR system on user-interested image retrieval generally requires complicated segmentation of the region from the background. In this paper, we propose a novel hierarchical-local-feature extraction scheme for CBIR, whereas complex image segmentation is avoided. In our CBIR system, a perception-based directional patch extraction method and an improved salient patch detection algorithm are proposed for local features extraction. Then, color moments and Gabor texture features are employed to index the salient regions. Extensive experiments have been performed to evaluate the performance of the proposed scheme, and experimental results show that the developed CBIR system produces plausible retrieval results.

Original languageEnglish
Pages (from-to)29099-29117
Number of pages19
JournalMultimedia Tools and Applications
Volume77
Issue number21
DOIs
Publication statusPublished - 1 Nov 2018

Keywords

  • Content-based image retrieval
  • Network big data
  • Perception-based directional patch
  • Salient patch detection

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

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