Color texture recognition in video sequences using wavelet covariance features and support vector machines

We pertain to the recognition of textural regions for color video analysis. The proposed scheme uses the covariance of 2^nd-order statistics on the wavelet domain, between the different color channels of the video frames. These features, named as color wavelet covariance (CWC), are used as color textural descriptors. A support vector machine was chosen for the classification of the CWC feature vectors. Experiments were conducted using both animated Vistex texture mosaics and standard video clips. The estimated average accuracy ranged from 90% to 97%. The results show that the proposed methodology could efficiently be used in various multimedia applications as a complete supervised color texture recognition system.