Result: An Approach to Improving Image Classification Using Visual Attention Weight Order
Title:
An Approach to Improving Image Classification Using Visual Attention Weight Order
Authors:
Source:
IPCV : proceedings of the 2011 international conference on image processing, computer vision, & pattern recognition (Las Vegas NV, July 18-21, 2011). :595-600
Publisher Information:
[S.l.]: CSREA Press, 2011.
Publication Year:
2011
Physical Description:
print, 21 ref
Original Material:
INIST-CNRS
Subject Terms:
Imagery, Imagerie fontionnelle et interventionnelle, Computer science, Informatique, Metrology and instrumentation, Métrologie et instrumentation, Optics, Optique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Sciences biologiques et medicales, Biological and medical sciences, Sciences biologiques fondamentales et appliquees. Psychologie, Fundamental and applied biological sciences. Psychology, Psychologie. Psychophysiologie, Psychology. Psychophysiology, Perception, Vision, Psychologie. Psychanalyse. Psychiatrie, Psychology. Psychoanalysis. Psychiatry, Analyse image, Image analysis, Análisis imagen, Attention visuelle, Visual attention, Atención visual, Image multiple, Multiple image, Imagen múltiple, Modélisation, Modeling, Modelización, Perception visuelle, Visual perception, Percepción visual, Psychologie cognitive, Cognitive psychology, Psichología cognitiva, Sémantique, Semantics, Semántica, Traitement image, Image processing, Procesamiento imagen, Vision ordinateur, Computer vision, Visión ordenador, Classification image, Image classification, Clasificación de imágenes
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
School of Computer Science and Technology, Tianjin University, Tianjin, China
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Psychology. Ethology
FRANCIS
Psychology. Ethology
FRANCIS
Accession Number:
edscal.26133748
Database:
PASCAL Archive
Further Information
Combined with visual attention mechanism and cognitive psychology, a new model of visual perception -VAWO (Visual Attention Weight Order) is proposed to model image regions based on multiple visual attention mechanism of human's eye. The focus area of the image can be obtained from the visual attention weight of image regions and the semantic expression of different regional object will be analyzed in the image. Experiments of image classification with Local semantic feature vector is presented and the results shows that the new model proposed in this paper can recognize the focus area of the image precisely and improve image classification in precision.