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Title: Pre-classification module for an all-season image retrieval system
Authors: Fu, H
Chi, ZG 
Feng, DD
Zou, W
Lo, KC
Zhao, X
Issue Date: 2007
Source: Proceedings of International Joint Conference on Neural Networks: IJCNN 2007: August 12-17, 2007, Orlando, Florida, USA, p. [1-5]
Abstract: From the study of attention-driven image interpretation and retrieval, we have found that an attention-driven strategy is able to extract important objects from an image and then focus the attentive objects while retrieving images. However, besides the images with distinct objects, there are images which do not show distinct objects. In this paper, the classification of "attentive" and "non-attentive" image is proposed to be a pre-process module in an all-season image retrieval system which can tackle both kinds of images. In this pre-classification module, an image is represented by an adaptive tree structure with each node carrying normalized features that characterize the object/region with visual contrasts and spatial information. Then a neural network is trained to classify an image as an "attentive" or "non-attentive" category by using the Back Propagation Through Structure (BPTS) algorithm. Experimental results indicate the reliability and feasibility of the pre-classification module, which encourages us to conduct further investigations on the all-season image retrieval system.
Keywords: Backpropagation
Feature extraction
Geophysics computing
Image classification
Image representation
Image retrieval
Neural nets
Tree data structures
Trees (mathematics)
Publisher: IEEE
ISBN: 1-4244-1380-X
DOI: 10.1109/IJCNN.2007.4371375
Rights: © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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