SIFT features in multiple color spaces for improved image classification
Document Type
Syllabus
Publication Date
1-1-2017
Abstract
This chapter first discusses oRGB-SIFT descriptor, and then integrates it with other color SIFT features to produce the Color SIFT Fusion (CSF), the Color Grayscale SIFT Fusion (CGSF), and the CGSF+PHOG descriptors for image classification with special applications to image search and video retrieval. Classification is implemented using the EFM-NN classifier, which combines the Enhanced Fisher Model (EFM) and the Nearest Neighbor (NN) decision rule. The effectiveness of the proposed descriptors and classification method is evaluated using two large scale and challenging datasets: the Caltech 256 database and the UPOL Iris database. The experimental results show that (i) the proposed oRGB-SIFT descriptor improves recognition performance upon other color SIFT descriptors; and (ii) the CSF, the CGSF, and the CGSF+PHOG descriptors perform better than the other color SIFT descriptors.
Identifier
85018513819 (Scopus)
Publication Title
Intelligent Systems Reference Library
External Full Text Location
https://doi.org/10.1007/978-3-319-52081-0_7
e-ISSN
18684408
ISSN
18684394
First Page
145
Last Page
166
Volume
121
Recommended Citation
Verma, Abhishek and Liu, Chengjun, "SIFT features in multiple color spaces for improved image classification" (2017). Faculty Publications. 9940.
https://digitalcommons.njit.edu/fac_pubs/9940
