GPCA-SIFT: A New Local Feature Descriptor for Scene Image Classification View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Lei Ju , Ke Xie , Hao Zheng , Baochang Zhang , Wankou Yang

ABSTRACT

In this paper, a new local feature descriptor called GPCA-SIFT is proposed for scene image classification. Like PCA-SIFT, we get the key points using the detection method in Scale Invariant Feature Transform (SIFT) and extract a 41 * 41 patch for each key point. Then we calculate the horizontal and vertical gradient of each pixel in the patch. However, instead of concatenating two gradient matrices, we directly work with the two-dimensional matrix and apply Generalized Principal Component Analysis (GPCA) to reduce it to a lower-dimensional matrix. Finally, we concatenate the reduced matrix and form a 1D vector. Compared with Principal Component Analysis (PCA), it preserves more spatial locality information. When applied in multi-class scene image classification, our proposed descriptor outperforms other related algorithms in terms of classification accuracy. More... »

PAGES

286-295

Book

TITLE

Pattern Recognition

ISBN

978-981-10-3004-8
978-981-10-3005-5

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-981-10-3005-5_24

DOI

http://dx.doi.org/10.1007/978-981-10-3005-5_24

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1004520306


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