Chaotic features for dynamic textures recognition

Abstract

This paper presents a novel framework for dynamic textures (DTs) modeling and recognition, investigating the use of chaotic features. We propose to extract chaotic features from each pixel intensity series in a video. The chaotic features in each pixel intensity series are concatenated to a feature vector, chaotic feature vector. Then, a video is modeled as a feature vector matrix. Next, two approaches of DTs recognition are investigated. A bag of words approach is used to represent each video as a histogram of chaotic feature vector. The recognition is carried out by 1-nearest neighbor classifier. We also investigate the use of earth mover's distance (EMD) method. Mean shift clustering algorithm is employed to cluster each feature vector matrix. EMD method is used to compare the similarity between two videos. The output of EMD matrix whose entry is the matching score can be used to DTs recognition. We have tested our approach on four datasets and obtained encouraging results which demonstrate the feasibility and validity of our proposed methods.