Name
Abstract | ||
|---|---|---|
Landmark image classification is a challenging task due to the various circumstances, e.g., illumination, viewpoint, zoom in/out and occlusion under which landmark images are taken. Most existing approaches utilize features extracted from the whole image including both landmark and non-landmark areas. However, non-landmark areas introduce redundant and noisy information. In this paper, we propose a novel approach to improve landmark image classification consisting of three steps. First, an attention-based 3-D reconstruction method is proposed to reconstruct sparse 3-D landmark models. Second, the sparse 3-D models are projected onto iconic images in order to identify images of the hot regions. For a landmark, hot regions are parts of a landmark which attract photographers' attention and are popularly captured in photos. These hot region images are later used to enhance reconstructed sparse 3-D models. Third, the landmark regions are obtained through mapping the enhanced 3-D models to landmark images. A k-dimensional tree (kd-tree) is then constructed for each landmark based on scale invariant feature transform (SIFT) features extracted from the landmark area to classify unlabeled images into pre-defined landmark categories. The proposed method is evaluated using 291 661 images of 51 landmarks. Experiments of comparison indicate that our method outperforms bag-of-words (BoW) based approach 18.5% and method of spatial-pyramid-matching using sparse-coding (ScSPM) 8.4%. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/TMM.2012.2190384 | IEEE Transactions on Multimedia |
Keywords | Field | DocType |
Three dimensional displays,Solid modeling,Image classification,Visualization,Image reconstruction,Feature extraction,Computational modeling | Iterative reconstruction,Computer vision,Scale-invariant feature transform,HOT Region,Pattern recognition,Computer science,Zoom,Feature extraction,Artificial intelligence,Contextual image classification,Landmark,Sparse matrix | Journal |
Volume | Issue | ISSN |
14 | 4 | 1520-9210 |
Citations | PageRank | References |
12 | 0.49 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xian Xiao | 1 | 108 | 7.50 |
| Changsheng Xu | 2 | 4957 | 332.87 |
| Jinqiao Wang | 3 | 804 | 89.03 |
| Min Xu | 4 | 268 | 28.42 |