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