Supervised Texture Classification Using a Novel Compression-Based Similarity Measure View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

Mehrdad J. Gangeh , Ali Ghodsi , Mohamed S. Kamel

ABSTRACT

Supervised pixel-based texture classification is usually performed in the feature space. We propose to perform this task in (dis)simil-arity space by introducing a new compression-based (dis)similarity measure. The proposed measure utilizes two dimensional MPEG-1 encoder, which takes into consideration the spatial locality and connectivity of pixels in the images. The proposed formulation has been carefully designed based on MPEG encoder functionality. To this end, by design, it solely uses P-frame coding to find the (dis)similarity among patches/images. We show that the proposed measure works properly on both small and large patch sizes. Experimental results show that the proposed approach significantly improves the performance of supervised pixel-based texture classification on Brodatz and outdoor images compared to other compression-based dissimilarity measures as well as approaches performed in feature space. It also improves the computation speed by about 40% compared to its rivals. More... »

PAGES

379-386

References to SciGraph publications

Book

TITLE

Computer Vision and Graphics

ISBN

978-3-642-33563-1
978-3-642-33564-8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-33564-8_46

DOI

http://dx.doi.org/10.1007/978-3-642-33564-8_46

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042712294


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