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27-11-2017 | Foundations

Learning visual codebooks for image classification using spectral clustering

Authors: Yi Hong, Weiping Zhu

Published in: Soft Computing | Issue 18/2018

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Abstract

This study explores the idea of learning visual codebook using spectral clustering, which we call spectral visual codebook learning (SVCL). Though spectral clustering has been widely applied into unsupervised segmentation, clustering, and manifold learning, using it to learn codebooks on standard image benchmark datasets has not been thoroughly studied. We show how learned codebooks by SVCL can be used for scene classification, texture recognition and image categorization. We describe several implementations for constructing the similarity graph and addressing the large-scale local image patches problem. We show that our approach captures nonlinear manifolds of semantic image patches. Another advantage is that both label and spatial information can be incorporated without increasing its model complexity. We validate SVCL on datasets such as KTH-TIPS, Scene-15, Graz-02, and Caltech-101.

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Title: Learning visual codebooks for image classification using spectral clustering
Authors: Yi Hong
Weiping Zhu
Publication date: 27-11-2017
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 18/2018
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-2937-4

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