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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorFu, H-
dc.creatorChi, ZG-
dc.creatorFeng, DD-
dc.creatorZou, W-
dc.creatorLo, KC-
dc.creatorZhao, X-
dc.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.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectFeature extractionen_US
dc.subjectGeophysics computingen_US
dc.subjectImage classificationen_US
dc.subjectImage representationen_US
dc.subjectImage retrievalen_US
dc.subjectNeural netsen_US
dc.subjectTree data structuresen_US
dc.subjectTrees (mathematics)en_US
dc.titlePre-classification module for an all-season image retrieval systemen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: Zheru Chien_US
dc.description.otherinformationAuthor name used in this publication: Dagan Fengen_US
dc.description.otherinformationCentre for Multimedia Signal Processing, Department of Electronic and Information Engineeringen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractFrom 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.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationProceedings of International Joint Conference on Neural Networks: IJCNN 2007: August 12-17, 2007, Orlando, Florida, USA, p. [1-5]-
dc.description.ros2007-2008 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
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