Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/37742
Title: Tree structures with attentive objects for image classification using a neural network
Authors: Fu, H
Zhang, S
Chi, Z 
Feng, D
Zhao, X
Keywords: Backpropagation
Image classification
Image segmentation
Tree data structures
Issue Date: 2009
Source: Proceedings of the 2009 International Joint Conference on Neural Networks (IJCNN'2009), Atlanta, GA, 14-19 June 2009, p. 898-902 How to cite?
Abstract: 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 ldquoimage + attentive-object + segmentsrdquo, ldquoimage + attentive-objectsrdquo, as well as ldquoimage + segmentsrdquo. Structure based neural networks are trained to classify the images by using the back propagation through structure (BPTS) algorithm. Experimental results show that the ldquoimage + attentive objectsrdquo 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.
URI: http://hdl.handle.net/10397/37742
ISBN: 978-1-4244-3548-7
978-1-4244-3553-1 (E-ISBN)
DOI: 10.1109/IJCNN.2009.5179021
Appears in Collections:Conference Paper

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