Object detection based on visual memory: a feature learning and feature imagination process. Issue 4 (20th April 2020)

• Record Type: Journal Article
• Authors: Dai, Houde; Lin, Mingqiang; Jiang, Wei
• Is Part Of: Enterprise information systems. Volume 14:Issue 4(2020)
• Page Start: 515
• Page End: 531
• Publication Date: 2020-04-20
• Subjects: Visual memory -- feature learning and feature imagination (FIFL) -- object detection -- top-down -- bottom-up; Information storage and retrieval systems -- Periodicals; Management information systems -- Periodicals; Electronic commerce -- Periodicals; 658.4038011
• DOI: 10.1080/17517575.2018.1539775
• Languages: English
• ISSNs: 1751-7575

Abstract

ABSTRACT: Visual memory plays an important role for the human's visual system to detect objects. The features of an object stored in the visual memory have much lower dimensions than the features contained within an image. We simulate the visual memory as a feature learning and feature imagination (FLFI) process to build an object detection algorithm. The method is constructed by a bottom-up feature learning and a top-down feature imagination. The proposed object detection method is tested using publicly available benchmark data sets, and the result indicates that it is fast and more robust.