Name
Abstract | ||
|---|---|---|
Saliency map analysis provides an alternative methodology to image semantic understanding in many applications such as adaptive content delivery and region-based image retrieval. A lot of visual saliency map algorithms have been proposed during the last decades. Recent psychophysical research further reveals that visual attention can be modulated and improved by the affective significance of stimuli, called Emotional Attention, which might not only supplement but also compete with other sources of top-down control on attention. Inspired by this mechanism, we propose a novel computational emotional attention model in this paper. In particular, we present an intuitive emotional saliency map computation method by calculating Minkowski-norm of pixel’s isolated saliency value and multi-scale local contrast information in color emotion space. Experimental results on diverse image datasets show that the proposed model can outperform some current state-of-the-art visual saliency map. |
Year | Venue | Field |
|---|---|---|
2016 | MMM | Computer vision,Saliency map,Content delivery,Pattern recognition,Computer science,Salience (neuroscience),Image retrieval,Attention model,Artificial intelligence,Pixel,Computation,Visual saliency |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinmiao Ding | 1 | 0 | 1.01 |
| Lulu Huang | 2 | 1 | 0.69 |
| Bing Li | 3 | 217 | 60.28 |
| Congyan Lang | 4 | 353 | 39.20 |
| Zhen Hua | 5 | 5 | 3.21 |
| Yuling Wang | 6 | 0 | 1.69 |