| Autor: |
Manikonda, L., Mangalampalli, A., Pudi, V. |
| Zdroj: |
2010 25th International Conference of Image & Vision Computing New Zealand; 1/ 1/2010, p1-8, 8p |
| Abstrakt: |
Uncertainty is inherently present in many real-world domains like images. Analyses of such uncertain data using traditional certain-data-oriented techniques do not achieve best possible accuracy. UACI introduces the concept of representing images in the form of a probabilistic or uncertain model using interest points in images. This model is an uncertain-data-based adaptation of Bag of Words, with each image not only represented by the visual words that it contains, but also their respective probabilities of occurrence in the image. UACI uses an Associative Classification approach to leverage latent frequent patterns in images for the identification of object classes. Unlike most image classifiers, which rely on positive and negative class sets (generally very vague) for training, UACI uses only positive class images for training. We empirically compare UACI with three other state-of-the-art image classifiers, and show that UACI performs much better than the other classifying approaches. [ABSTRACT FROM PUBLISHER] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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