Image Feature Extraction Based on Compressive Sensing with Application of Image Denoising

Feature extraction for object representation plays an important role in image denoising system. As the spatial histograms consist of marginal distribution of image over local patches, object texture and shape are simultaneously preserved by the spatial histogram representation. In this paper, we propose methods of compressive sensing for spatial histogram-based object detection. We employ Fisher criterion to measure the discriminability of each spatial histogram feature and calculate features correlation using mutual information. In order to construct compact feature sets for efficient classification, we propose informative selection algorithm to select uncorrelated and discriminative spatial histogram features. The proposed approaches are tested on two different kinds of images. The experimental results show that the proposed approaches are efficient in feature extraction and image denoising.