Learning informative features for spatial histogram-based object detection
Feature extraction for object representation plays an important role in automatic object detection 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 learning informative features 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 objects: car and video text. The experimental results show that the proposed approaches are efficient in object detection.