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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.