Feature classification based on local information

Feature extraction is the first and most critical step in various vision applications. The detected features must be classified into different feature types before they can be efficiently and effectively applied on further vision tasks. In this paper, we propose a feature classification algorithm that classifies the detected regions into four types including blobs, edges and lines, textures, and texture boundaries, by using the correlations with the neighbouring regions. The effectiveness of the feature classification is evaluated on image retrieval.