Name
Abstract | ||
|---|---|---|
Spatial pyramid matching (SPM) uses the statistics of local features in an image sub region as a global feature. It shows good performance in terms of generic image recognition. However, the disadvantages of this method are that the constructed visual dictionary is easy to fall into a local optimal solution due to the randomness of the initial centroid of k-means and it ignores the spatial distribution of salient object in images. In this research, we propose a new clustering method that using black hole algorithm to determine the initial center of k-means when constructing a visual dictionary and making the result have globally optimal solution and less computational costs. To better distinguish the target and background in the image, we propose discriminative SPM, which is a new representation that forms the image feature as a weighted sum of features over all pyramid levels. The weights are selected by the spatial distribution of salient objects in images. The resulting feature is compact and preserves high discriminative power. Thus reducing the effect of image background on classification. As documented in the experimental results, the proposed schemes can improve the classification accuracy of image compared to the other existing methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-95933-7_20 | INTELLIGENT COMPUTING THEORIES AND APPLICATION, PT II |
Keywords | Field | DocType |
K-means, Black hole algorithm, Spatial pyramid, Initial center, Salient object, Discriminative SPM | k-means clustering,Pattern recognition,Computer science,Artificial intelligence,Pyramid,Contextual image classification,Cluster analysis,Discriminative model,Centroid,Visual dictionary,Randomness | Conference |
Volume | ISSN | Citations |
10955 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Feng | 1 | 25 | 4.89 |
| Xiaofeng Wang | 2 | 4 | 10.54 |
| Dongfang Chen | 3 | 0 | 1.35 |