Name
Abstract | ||
|---|---|---|
Image scene classification, the classification of images into semantic categories, e.g. city, urban, sea, etc, has recently become a vigorous research focus in computer vision for its broad application prospect. In this paper, we propose a novel approach to understand image semantic scene based on multi-bag-of-features. We aim to design an efficient but simple scene classification algorithm via fusing multiple low-level image features. Experimental results demonstrate that the proposed approach offers an effective way to classify the complex image scenes by using a multi-bag-of-features model. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICMLC.2011.6017012 | ICMLC |
Keywords | Field | DocType |
image scene classification,multi-bag-of-features,svm classifier,image classification,support vector machine,support vector machines,quantization,image segmentation,feature extraction,image features,computer vision | Computer science,Image segmentation,Artificial intelligence,Contextual image classification,Image-based modeling and rendering,Categorization,Computer vision,Pattern recognition,Feature (computer vision),Support vector machine,Scene statistics,Feature extraction,Machine learning | Conference |
Volume | Issue | ISSN |
4 | null | 21601348 |
Citations | PageRank | References |
5 | 0.44 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weifeng Zhang | 1 | 29 | 8.24 |
| Zengchang Qin | 2 | 439 | 45.46 |
| Tao Wan | 3 | 181 | 21.18 |