Hierarchical web image classification by multi-level features

The hierarchical image classification of web content is still an open issue in the research fields. In this paper, we address the problem of image classification by using not only the low-level perceptual features, but also the high-level semantics features. We focus on the robustness and efficiency of image classification by different categorization methods on different features sets. Our experiments reveal some characteristics in the hierarchy classification based on textual and visual features. We propose a hierarchical threshold strategy based on data structure for multi-class categorization. The evaluation results are reported and discussed.