TY - JOUR
T1 - CoMo: a scale and rotation invariant compact composite moment-based descriptor for image retrieval
AU - Vassou, S. A.
AU - Anagnostopoulos, N.
AU - Christodoulou, K.
AU - Amanatiadis, A.
AU - Chatzichristofis, S. A.
N1 - Publisher Copyright:
© 2018 Springer Science+Business Media, LLC, part of Springer Nature
PY - 2018/3/15
Y1 - 2018/3/15
N2 - Low level features play a significant role in image retrieval. Image moments can effectively represent global information of image content while being invariant under translation, rotation, and scaling. This paper presents CoMo: a moment based composite and compact low-level descriptor that can be used effectively for image retrieval and robot vision tasks. The proposed descriptor is evaluated by employing the Bag-of-Visual-Words representation over various well-known benchmarking image databases. The findings from the experimental evaluation provide strong evidence of high and competitive retrieval performance against various state-of-the-art local descriptors.
AB - Low level features play a significant role in image retrieval. Image moments can effectively represent global information of image content while being invariant under translation, rotation, and scaling. This paper presents CoMo: a moment based composite and compact low-level descriptor that can be used effectively for image retrieval and robot vision tasks. The proposed descriptor is evaluated by employing the Bag-of-Visual-Words representation over various well-known benchmarking image databases. The findings from the experimental evaluation provide strong evidence of high and competitive retrieval performance against various state-of-the-art local descriptors.
KW - Compact composite descriptors
KW - Content based image retrieval
KW - Low level features
UR - https://www.scopus.com/pages/publications/85044056447
U2 - 10.1007/s11042-018-5854-3
DO - 10.1007/s11042-018-5854-3
M3 - Article
SN - 1380-7501
SP - 1
EP - 24
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
ER -