Coordinate coding on the riemannian manifold of symmetric positive-definite matrices for image classification

Harandi, Mehrtash, Basirat, Mina and Lovell, Brian C. (2015). Coordinate coding on the riemannian manifold of symmetric positive-definite matrices for image classification. In Pavan Turaga and Anuj Srivastava (Ed.), Riemannian computing in computer vision (pp. 345-361) Cham, Switzerland: Springer. doi:10.1007/978-3-319-22957-7_16


Author Harandi, Mehrtash
Basirat, Mina
Lovell, Brian C.
Title of chapter Coordinate coding on the riemannian manifold of symmetric positive-definite matrices for image classification
Title of book Riemannian computing in computer vision
Place of Publication Cham, Switzerland
Publisher Springer
Publication Year 2015
Sub-type Research book chapter (original research)
DOI 10.1007/978-3-319-22957-7_16
Open Access Status Not yet assessed
Year available 2015
ISBN 9783319229577
9783319229560
Editor Pavan Turaga
Anuj Srivastava
Start page 345
End page 361
Total pages 17
Language eng
Subjects 2200 Engineering
1700 Computer Science
2600 Mathematics
Abstract/Summary Over the years, coding—in its broadest definition—has proven a crucial step in visual recognition systems [4, 7]. Many techniques have been investigated, such as bag of words [1, 9, 16, 18, 19, 31], sparse coding [21, 34], and locality-based coding[33, 35]. All these techniques follow a similar flow: Given a dictionary of code words, a query is associated to one or multiple dictionary elements with different weights (i.e. let@tokeneonedot, binary or real). These weights, or codes, act as the new representation for the query and serve as input to a classifier (i.e., support vector machine (SVM)) after an optional pooling step.
Q-Index Code B1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Sat, 23 Dec 2017, 16:35:19 EST