Towards reliable object representation via sparse directional patches and spatial center cues

Muwei Jian*, Hui Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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In the process of image understanding, the human visual system (HVS) performs multiscale analysis on various objects. HVS primarily focuses on marginally conspicuous image patches located within or around distinct objects rather than scanning the image pixels point by point. Inspired by the HVS mechanism, in this paper, we aimed to describe and exploit multiscale decomposition-based patch detection models for automatic visual feature representation and object localization in images. Our investigation into mimicking and modeling the HVS to capture conspicuous sparse patches and their spatial distribution clues makes a profound contribution to the automatic comprehension and characterization of images by machines. This study demonstrates that the sparse patch-based visual representation with spatial center cues is intrinsically tolerant to object positioning and understanding beyond object variations in spatial position, multiresolution, and chrominance, which has significant implications for many vision-based automatic object grabbing and perception applications, such as robotics, human‒machine interaction, and unmanned aerial vehicles (UAVs).

Original languageEnglish
Number of pages6
JournalFundamental Research
Early online date10 Aug 2023
Publication statusEarly online - 10 Aug 2023


  • image patches
  • multiscale analysis
  • object representation
  • Shearlet transform
  • visual perception

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