Can a machine have two systems for recognition, like human beings?

Jiwei Hu, Kin Man Lam, Ping Lou, Quan Liu, Wupeng Deng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Artificial Intelligence has attracted much of researchers’ attention in recent years. A question we always ask is: “Can machines replace human beings to some extent?” This paper aims to explore the knowledge learning for an image-annotation framework, which is an easy task for humans but a tough task for machines. This paper's research is based on an assumption that machines have two systems of thinking, each of which handles the labels of images at different abstract levels. Based on this, a new hierarchical model for image annotation is introduced. We explore not only the relationships between the labels and the features used, but also the relationships between labels. More specifically, we divide labels into several hierarchies for efficient and accurate labeling, which are constructed using our Associative Memory Sharing method, proposed in this paper.

Original languageEnglish
Pages (from-to)275-286
Number of pages12
JournalJournal of Visual Communication and Image Representation
Volume56
DOIs
Publication statusPublished - Oct 2018

Keywords

  • Feature-pool selection
  • Hierarchical tree structure
  • Image annotation
  • Multi-labeling

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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