Name
Abstract | ||
|---|---|---|
This paper analyzes the use of visual words, as low-level image features, for learning and categorizing images. We show that this problem can be reduced to a simultaneous weighting of appropriate features and detection of clusters in a binary feature space. A probabilistic model is then proposed to quantify the effectiveness of visual words when treated as binary features. In order to learn the model, we consider a maximum a posteriori (MAP) approach. Experimental results are presented to illustrate the feasibility and merits of our approach. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/MMSP.2009.5293316 | Rio De Janeiro |
Keywords | Field | DocType |
feature extraction,maximum likelihood estimation,probability,MAP approach,binary probabilistic model,categorizing images,clusters detection,learning concepts,low-level image features,maximum a posteriori approach,visual scenes,visual words | Weighting,Computer science,Artificial intelligence,Computer vision,Feature vector,Pattern recognition,Visualization,Feature (computer vision),Feature extraction,Statistical model,Maximum a posteriori estimation,Machine learning,Visual Word | Conference |
ISSN | ISBN | Citations |
2163-3517 | 978-1-4244-4464-9 | 3 |
PageRank | References | Authors |
0.42 | 15 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nizar Bouguila | 1 | 1539 | 146.09 |
| Khalid Daoudi | 2 | 145 | 23.68 |