Name
Abstract | ||
|---|---|---|
Color perception is one of the major cognitive abilities of human being. Color information is also one of the most important features in various computer vision tasks including object recognition, tracking, scene classification and so on. In this paper, we proposed a simple and effective method for learning color composition of objects from large annotated datasets. The new proposed model is based on a region-based bag-of-colors model and saliency detection. The effectiveness of the model is empirically verified on manually labelled datasets with single or multiple tags. The significance of this research is that the color information of an object can provide useful prior knowledge to help improving the existing computer vision models in image segmentation, object recognition and tracking. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/VCIP.2013.6706433 | VCIP |
Keywords | Field | DocType |
color information,scene classification,object color,region-based bag-of-colors,image segmentation,human being,saliency detection,object tracking,color perception,object recognition,colour vision,computer vision,bag-of-colors,color histogram,image colour analysis | Computer vision,3D single-object recognition,Color histogram,Object-class detection,Bag-of-words model in computer vision,Pattern recognition,Computer science,Artificial intelligence,False color,Color normalization,Color quantization,Color image | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4799-0288-0 |
Citations | PageRank | References |
0 | 0.34 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaofan Zhang | 1 | 20 | 4.14 |
| Zengchang Qin | 2 | 439 | 45.46 |
| Xu Liu | 3 | 10 | 1.53 |
| Tao Wan | 4 | 181 | 21.18 |