Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1-24 |
| Number of pages | 24 |
| Journal | Multimedia Tools and Applications |
| DOIs | |
| Publication status | Published - 15 Mar 2018 |
Keywords
- Compact composite descriptors
- Content based image retrieval
- Low level features
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