Name
Abstract | ||
|---|---|---|
In this paper we propose a new algorithm for region-based image categorization that is formulated as a Multiple-Instance Learning (MIL) problem. The proposed algorithm transforms the MIL problem into a traditional supervised learning problem, and solves it using a standard supervised learning method. The features used in the proposed algorithm are the hyperclique patterns which are "condensed" into a small set of discriminative features. Each hyperclique pattern consists of multiple strongly-correlated instances (i.e., features). As a result, hyperclique patterns are able to capture the information that are not shared by individual features. The advantages of the proposed algorithm over existing algorithms are threefold: (i) unlike some existing algorithms which use learning methods that are specifically designed for MIL or for certain datasets, the proposed algorithm uses a general-purpose standard supervised learning method, (ii) it uses a significantly small set of features which are empirically more discriminative than the PCA features (i.e. principal components), and (iii) it is simple and efficient and achieves a comparable performance to most state-of-the-art algorithms. The efficiency and good performance of the proposed algorithm make it a practical solution to general MIL problems. In this paper, we apply the proposed algorithm to both drug activity prediction and image categorization, and promising results are obtained. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/MMSP.2008.4665145 | MMSP |
Keywords | Field | DocType |
image processing,learning (artificial intelligence),principal component analysis,PCA features,feature set reduction,general-purpose standard supervised learning method,hyperclique patterns,multiple instance learning,region-based image categorization | Data mining,Categorization,Algorithm design,Pattern recognition,Computer science,Image processing,Supervised learning,Image segmentation,Artificial intelligence,Statistical classification,Cluster analysis,Discriminative model | Conference |
Citations | PageRank | References |
11 | 0.50 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gunawan Herman | 1 | 48 | 4.00 |
| Getian Ye | 2 | 81 | 9.47 |
| Jie Xu | 3 | 64 | 8.22 |
| Bang Zhang | 4 | 111 | 12.40 |