Tree structures with attentive objects for image classification using a neural network

Hong Fu, Shuya Zhang, Zheru Chi, David Dagan Feng, Xiaoyu Zhao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


This paper presents an image classification method based on a neural network model dealing with tree structures of attentive objects. Apart from regions provided by image segmentation, attentive objects, which are extracted from a segmented image by an attention-driven image interpretation algorithm, are used to construct the tree structure to represent an image. Three combinations of tree structures are investigated, including "image + attentive-object + segments", "image + attentive-objects", as well as "image + segments". Structure based neural networks are trained to classify the images by using the Back Propagation Through Structure (BPTS) algorithm. Experimental results show that the "image + attentive objects" structure is more favorable, comparing with both the other two structures proposed by us and a start-of-art tree structure reported in the literature, in terms of classification rate and computational time.
Original languageEnglish
Title of host publication2009 International Joint Conference on Neural Networks, IJCNN 2009
Number of pages5
Publication statusPublished - 18 Nov 2009
Event2009 International Joint Conference on Neural Networks, IJCNN 2009 - Atlanta, GA, United States
Duration: 14 Jun 200919 Jun 2009


Conference2009 International Joint Conference on Neural Networks, IJCNN 2009
Country/TerritoryUnited States
CityAtlanta, GA

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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