Name
Abstract | ||
|---|---|---|
Abstract A visual attention system should respond placidly when,common stimuli are pre- sented, while at the same time keep alert to anomalous visual inputs. In this paper, a dynamic,visual attention model based on the rarity of features is proposed. We introduce the Incremental Coding Length (ICL) to measure,the perspective en- tropy gain of each feature. The objective of our model is to maximize the entropy of the sampled visual features. In order to optimize energy consumption, the limit amount,of energy of the system is re-distributed amongst features accord- ing to their Incremental Coding Length. By selecting features with large coding length increments, the computational system can achieve attention selectivity in both static and dynamic,scenes. We demonstrate that the proposed model achieves superior accuracy in comparison,to mainstream,approaches in static saliency map generation. Moreover, we also show that our model captures several less-reported dynamic visual search behaviors, such as attentional swing and inhibition of re- turn. |
Year | DOI | Venue |
|---|---|---|
2008 | null | Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference |
Keywords | Field | DocType |
visual search | Visual search,Computer science,Human visual system model,Coding (social sciences),Artificial intelligence,Swing,Computer vision,Kadir–Brady saliency detector,Pattern recognition,Inhibition of return,Visual attention,Energy consumption,Machine learning | Conference |
Volume | Issue | ISSN |
null | null | null |
Citations | PageRank | References |
206 | 7.97 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaodi Hou | 1 | 2069 | 72.53 |
| Liqing Zhang | 2 | 2713 | 181.40 |