Beyond Spatial Pyramid Matching: Spatial Soft Voting for Image Classification View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Toshihiko Yamasaki , Tsuhan Chen

ABSTRACT

Recently, spatial partitioning approaches such as spatial pyramid matching (SPM) are commonly used in image classification to collect the global and local features of the images. They divide the input image into small sub-regions (typically in a hierarchical manner) and generate a feature vector for each of them. Although the codes for the descriptors are assigned softly in modern image feature representation techniques, the codes must fall into only a single sub-region when forming the feature vector. In other words, the soft code assignment is used in the descriptor space but the codes are still “hard” voted from the view point of the image space. This paper proposes a spatial soft voting method, in which the existence of the codes are expressed by a Gaussian function and the maps of the existence are sampled to form a feature vector. The generated feature vectors are “soft” both in the descriptor space and the image space. In addition, extra computational cost as compared to SPM is negligibly small. The concept of the spatial soft voting is general and can be applied to most hard spatial partitioning approaches. More... »

PAGES

506-519

References to SciGraph publications

Book

TITLE

Computer Vision - ACCV 2012 Workshops

ISBN

978-3-642-37483-8
978-3-642-37484-5

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-37484-5_41

DOI

http://dx.doi.org/10.1007/978-3-642-37484-5_41

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038894980


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