Feature Selection for Image Categorization

Abstract

Image classification could be treated as an effective solution to enable keyword-based semantic image retrieval, while feature selection is a key issue in categorization. In this paper, we propose a novel strategy by using feature selection in learning semantic concepts of image categories. To choose representative and informative features for an image category and meanwhile reduce noisy features, a feature selection strategy is proposed. In the feature selection stage, salient patches are first detected by SIFT descriptor and clustered by DENCLUE algorithm. Then the pointwise mutual information between the salient patches and the image category is calculated to evaluate the important patches and construct the visual vocabulary for the category. Based on the selected visual features, the SVM classifier is applied to categorization. The experimental results on Corel image database demonstrate that the proposed feature selection approach is very effective in image classification and visual concept learning.KeywordsFeature SelectionImage CategoryScale Invariant Feature TransformSemantic ConceptFeature Selection AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.