Name
Abstract | ||
|---|---|---|
Similarity measure is an important research topic in image classification and retrieval. Given a type of image features, a good similarity measure should be able to retrieve similar images from the database while discard irrelevant images from the retrieval. Similarity measures in literature are typically distance based which measure the spatial distance between two feature vectors in high dimensional feature space. However, this type of similarity measures do not have any perceptual meaning and ignore the neighborhood influence in the similarity decision making process. In this paper, we propose a novel dissimilarity measure, which can measure both the distance and perceptual similarity of two image features in feature space. Results show the proposed similarity measure has a significant improvement over the traditional distance based similarity measure commonly used in literature. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IVCNZ.2018.8634763 | IVCNZ |
Keywords | Field | DocType |
Image color analysis,Histograms,Image retrieval,Transforms,Australia,Standards | Histogram,Computer vision,Feature vector,Pattern recognition,Similarity measure,Feature (computer vision),Computer science,Image retrieval,Artificial intelligence,Contextual image classification,Perception,Perceptual similarity | Conference |
ISSN | ISBN | Citations |
2151-2191 | 978-1-7281-0125-5 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamid Shojanazeri | 1 | 0 | 0.68 |
| Dengsheng Zhang | 2 | 2462 | 100.00 |
| Shyh Wei Teng | 3 | 151 | 21.02 |
| Sunil Aryal | 4 | 38 | 8.23 |
| Guojun Lu | 5 | 124 | 9.01 |