Name
Title | ||
|---|---|---|
Large-scale Outdoor Scene Classification by Boosting a Set of Highly Discriminative and Low Redundant Graphlets |
Abstract | ||
|---|---|---|
Large-scale outdoor scene classification is an important issue in multimedia information retrieval. In this paper, we propose an efficient scene classification model by integrating outdoor scene image's local features into a set of highly discriminative and less redundant graph lets (i.e., small connected sub graph). Firstly, each outdoor scene image is segmented into a number of regions in terms of its color intensity distribution. And a region adjacency graph (RAG) is defined to encode the geometric property and color intensity distribution of outdoor scene image. Then, the frequent sub-structures are mined statistically from the RAGs corresponding to the training outdoor scene images. And a selecting process is carried out to obtain a set of sub-structures from the frequent ones towards being highly discriminative and low redundant. And these selected sub-structures are used as templates to extract the corresponding graph lets. Finally, we integrate these extracted graph lets by a multi-class boosting strategy for outdoor scene classification. The experimental results on the challenging SUN [1] data set and the LHI [14] data set validate the effectiveness of our approach. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICDMW.2011.108 | ICDM Workshops |
Keywords | Field | DocType |
graphlets,training outdoor scene image,highly discriminative,multimedia computing,low redundant graphlets,region adjacency graph,outdoor scene image local feature,multiclass boosting strategy,discriminative graphlets,statistical analysis,learning (artificial intelligence),redundant graphlets,large-scale outdoor scene classification,small connected sub graph,image segmentation,color intensity distribution,statistical mining,geometric property,outdoor scene image training,large scale outdoor scene classification,multimedia information retrieval,outdoor scene image,outdoor scene images,redundant graph,feature extraction,image classification,image retrieval,rag,data mining,graph theory,feature selection,efficient scene classification model,corresponding graph,outdoor scene classification,learning artificial intelligence | Adjacency list,Computer science,Image retrieval,Multimedia information retrieval,Artificial intelligence,Contextual image classification,Discriminative model,Graph theory,Computer vision,Pattern recognition,Feature extraction,Boosting (machine learning),Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4673-0005-6 | 1 | 0.35 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luming Zhang | 1 | 1126 | 60.35 |
| Mingli Song | 2 | 1646 | 98.10 |
| Xiaoyu Deng | 3 | 31 | 3.74 |
| Jiajun Bu | 4 | 4106 | 211.52 |
| Chun Chen | 5 | 4727 | 246.28 |