NDLI: A Topology Preserving Approach for Image Classification

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A Topology Preserving Approach for Image Classification

Content Provider	IEEE Xplore Digital Library
Author	Le Dong Izquierdo, E.
Copyright Year	2007
Abstract	In this paper, an approach for image analysis and classification is presented. It is based on a topology preserving approach to automatically create a relevance map from salient areas in natural images. It also derives a set of well-structured representations from low-level description to drive the final classification. The backbone of this approach is a distribution mapping strategy involving two basic modules: structured low-level feature extraction using convolution neural network and a topology preservation module based on a growing neural gas network. Classification is achieved by simulating the high-level top-down visual information perception in primates followed by incremental Bayesian parameter estimation. The proposed modular system architecture offers straightforward expansion to include user relevance feedback, contextual input, and multimodal information if available.
Starting Page	7
Ending Page	7
File Size	241996
Page Count	1
File Format	PDF
ISBN	076952818X
DOI	10.1109/WIAMIS.2007.13
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-06
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image analysis Parameter estimation Convolution Network topology Bayesian methods Spine Neural networks Feature extraction Neurofeedback Image classification
Content Type	Text
Resource Type	Article

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