Using Frequent Pattern Mining for Image Labeling
利用频繁模式挖掘进行图像标注

One major challenge in the content-based image retrieval and computer vision research is to relate low-level visual features with semantic concepts, that is, to extract semantic concepts from an image effectively according to its low-level visual features. Especially when images contain more than one concept, the problem will be even more intractable. In this paper, we provide a method to label an image based on the frequent patterns of its low-level visual features. According to the specialty of image segmentation, effectivealgorithms are implemented to mine such patterns and to generate labeling rules. It is shown in the experiments on authoritative and real datasets that our method is more effective than some previously proposed methods for labeling images containing multiple concepts.